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Last updated: 2024-02-09

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Knit directory: scSeq_Hefendehl/

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absolute	relative
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---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/03_New_AnalysesforPub.Rmd) and HTML (docs/03_New_AnalysesforPub.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	dcc077d	achiocch	2024-02-09	workflowr::wflow_publish(c("./analysis/03", "./analysis/index"))
Rmd	50d24c0	Andreas Chiocchetti	2023-11-03	handover JH
Rmd	2290b23	Andreas Chiocchetti	2023-11-03	backup 20231103

---

RNA seq wishlist

Reclustering of dataset from Desiree

1. Exclude sex-related genes – maybe this will already resolve our issues concerning the clustering in terms of biological relevance.
2. In case the new clusters are not matching the biological relevance, reclustering based on lists we provide for homeostatic and reactive microglial cells

if you want to save a plot use ggsave(filename = “./output/PNametheplot.svg”, plotobject))

Define Gene lists

DAM_up <- c("Itgax","Cst7","Ccl4","Clec7a","Lpl","Siglec1","Spp1","Axl","Apoe","Trem2","Csf1","Lyz2","H2-D1","Tyrobp","Cd74","Gpnmb","B2m","Cd9","Ctsb","Ctsd") # genes up regulated in DAMs versus homestatics

# additions
DAM1_up <- c("Tyrobp", "Ctsb", "Ctsd", "Apoe", "B2m", "Fth1", "Lyz2") # genes up regulated in DAM1 vs Homeostasis
DAM1_down <- c("Cx3cr1", "P2ry12", "Tmem119")# genes down regulated in DAM1 vs homeostasis
DAM2_up <- c("Trem2", "Axl", "Cst7", "Ctsl", "Lpl", "Cd9", "Csf1", "Ccl6", "Itgax", "Clec7a", "Lilrb4", "Timp2")

DAM1vsDAM2 = c("Trem2", "Cst7", "Spp1", "Lpl", "Itgax", "Ctsl", "Ctsz", "Cd68", "Axl", "Cd9", "Ccl6", "Csf1")

homeostat.genes=c("P2ry12","P2ry13","Tmem119","Cx3cr1","Selplg","Cd33")

cluster.genes=unique(c(DAM_up, DAM1vsDAM2, homeostat.genes))

Spatial cell types

ngenes = 50
distal_dat = openxlsx::read.xlsx("/files/scSeq_Hefendehl/data/Copy of DEG_control_vs_stroke_vs_distal_original.xlsx", sheet = 1)
distal_dat <- distal_dat[order(distal_dat$score, decreasing = T)[1:ngenes],]
distal_genes <- distal_dat$X1

control_dat = openxlsx::read.xlsx("/files/scSeq_Hefendehl/data/Copy of DEG_control_vs_stroke_vs_distal_original.xlsx",sheet = 2)
control_dat <- control_dat[order(control_dat$score, decreasing = T)[1:ngenes],]
control_genes <- control_dat$X1

Stroke_dat = openxlsx::read.xlsx("/files/scSeq_Hefendehl/data/Copy of DEG_control_vs_stroke_vs_distal_original.xlsx",sheet = 3)
Stroke_dat <- Stroke_dat[order(Stroke_dat$score, decreasing = T)[1:ngenes],]
Stroke_genes <- Stroke_dat$X1

3. Mapping cells of “spatially identified signature” onto the new plots

Rechecking exclusion of cells

Some cells appear to have a low expression of Aif1 as can be seen in the purple shading. However, some of these cells with the low expression seem to be missing later on (see Clustering example). Please check if really only cells with an expression of 0 Aif1 have been excluded

inspect unfiltered original RAW data

# cluster all cells for visualization purpose prior to exclusion 

DefaultAssay(samples.integrated) = "RNA"
samples.integrated <- ScaleData(samples.integrated)

Centering and scaling data matrix

samples.integrated <- FindVariableFeatures(samples.integrated)
samples.integrated<- RunHarmony(samples.integrated, group.by.vars="Plate", npcs = 50)

Warning: Warning: The parameters do_pca and npcs are deprecated. They will be ignored for this function call and please remove parameters do_pca and npcs and pass to harmony cell_embeddings directly.
This warning is displayed once per session.

Transposing data matrix

Initializing state using k-means centroids initialization

Harmony 1/10

Harmony 2/10

Harmony 3/10

Harmony 4/10

Harmony 5/10

Harmony 6/10

Harmony 7/10

Harmony 8/10

Harmony converged after 8 iterations

samples.integrated<- RunPCA(samples.integrated, npcs = 50)

PC_ 1 
Positive:  S100a8, S100a9, S100a11, Slfn1, Cxcr2, Ifitm2, Fxyd5, Clec4d, Mmp9, Mcemp1 
       Cd44, Ifitm1, Gsr, Iqgap1, Sell, Hp, Slpi, Pglyrp1, Cd24a, Il1r2 
       Gda, Gsn, Lrg1, Mxd1, Emb, Adgre5, 4833407H14Rik, Anxa2, Sirpb1c, Lilr4b 
Negative:  Gm10800, Gm26870, Gm10801, Tmem206, Cd83, Xist, Apoe, P3h2, Prmt9, Cdc23 
       Golt1b, Zfp62, Cst7, Entpd6, Ptprm, Zfp280d, Gna11, Gm42944, Gnpda2, Lpl 
       Dnajc18, Lman2l, Rnf139, Zfp746, Sec22b, Ufl1, Smg5, Gm28265, Nrep, Slc33a1 
PC_ 2 
Positive:  Cybb, Clec12a, Mki67, Cdca3, Ly6c2, Napsa, Chil3, Ifitm6, Mgst1, S100a4 
       Serpinb1a, Ccna2, Ccnb2, Crip1, Ube2c, Lcn2, Cenpa, Stmn1, Arhgef39, Top2a 
       H2-Eb1, Nusap1, Aurka, H2-Ab1, Smc2, Wfdc21, Lgals1, Plac8, H2-Aa, Cd74 
Negative:  Clec4d, Cxcr2, 4833407H14Rik, Il1f9, Il1b, Ifitm1, Sirpb1b, Sfxn5, Slfn1, H2-Q10 
       Cd300ld, Gm26809, Slfn4, Mirt1, Acod1, Amer2, Mxd1, Mmp9, Tnfaip2, Il1rn 
       Il18rap, Ccr1, Sirpb1c, Sirpb1a, Osgin1, Pla2g7, Il1r2, Sell, Gm5150, Hcar2 
PC_ 3 
Positive:  Wfdc21, Lcn2, Serpinb1a, Cdca3, Hp, Ly6c2, S100a9, Chil3, Mki67, Ccnb2 
       Top2a, Pglyrp1, Ube2c, Ccna2, Stmn1, Hsd11b1, Ngp, S100a8, Mgst1, Arhgef39 
       Ltf, Ltb4r1, Smc2, Anxa1, Knl1, Chil1, Spc25, Gm10801, Nusap1, Cdca8 
Negative:  Cd74, H2-Eb1, Crip1, H2-Aa, H2-Ab1, S100a4, Apoe, Ccr2, Ciita, Mndal 
       Lgals1, Ahnak, Ifi211, Ifi47, Emilin2, Olfm1, Wfdc17, Pltp, Fxyd5, Lyz2 
       Cst7, Ifi209, Slamf7, Apoc2, Ifit3, Acot7, Usp18, Itgb7, Lgals3, Oas2 
PC_ 4 
Positive:  Gm10801, Gm26870, Gm10800, S100a4, Ccr2, Ahnak, Itgb7, Ciita, Gm42944, Slc9a7 
       Gimap3, Cd3e, AC138284.4, Emb, Adamts6, Gimap5, Mndal, Lat, Gm28265, Zfp729a 
       Zfp280d, Tecpr2, 1110002L01Rik, Tbc1d10c, Gm2916, Ccdc88c, Prmt9, Tgfbi, C130089K02Rik, Rasgrp1 
Negative:  Apoe, Cst7, Lyz2, Ccl6, Ccl3, Ch25h, Cd83, Lilrb4a, Spp1, Ccl4 
       Actr3b, Hcar2, Ccnb2, Cybb, Apoc1, Wfdc17, Lpl, Cdkn1a, Egr2, Arrdc4 
       Ccna2, Aurka, Wfdc21, Dcstamp, Ube2c, C3, Gpx3, Cd74, Stmn1, Mki67 
PC_ 5 
Positive:  Ccnb2, Ube2c, Kif22, Smc2, Stmn1, Srgn, Cdk1, Nusap1, Aurka, Ccna2 
       Mki67, Top2a, Spc25, Bub1, Cep55, Ufl1, Zfp62, Cdca3, Mcm5, Hmgn2 
       Jak1, Haspin, Cdkn1a, Smc4, Gm38287, Zfp41, Cks1b, Knl1, Zfp758, Kif20a 
Negative:  Gm10801, Gm10800, Gm26870, Gm28265, Ltf, Ifitm6, Ly6c2, Ngp, Serpinb1a, Mgst1 
       Lcn2, Chil3, Itgb2l, Wfdc21, Fpr2, Camp, I830127L07Rik, Clec12a, Cd177, Hp 
       Plac8, Nanos1, AC147565.1, Pilrb2, Ntm, Serpinb10, Ltb4r1, Rab44, Pglyrp1, Napsa 

ElbowPlot(samples.integrated, ndims = 50)

samples.integrated <- JackStraw(samples.integrated, assay = "RNA")
samples.integrated <- ScoreJackStraw(samples.integrated, dims = 1:20)
JackStrawPlot(samples.integrated, dims = 1:20)

Warning: Removed 31636 rows containing missing values (`geom_point()`).

samples.integrated<- RunUMAP(samples.integrated, reduction = "harmony", dims = 1:20)

Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session

16:38:54 UMAP embedding parameters a = 0.9922 b = 1.112

Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'

Also defined by 'spam'

16:38:54 Read 905 rows and found 20 numeric columns

16:38:54 Using Annoy for neighbor search, n_neighbors = 30

Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'

Also defined by 'spam'

16:38:54 Building Annoy index with metric = cosine, n_trees = 50

0%   10   20   30   40   50   60   70   80   90   100%

[----|----|----|----|----|----|----|----|----|----|

**************************************************|
16:38:54 Writing NN index file to temp file /tmp/RtmpdRmKoZ/filef8230915521
16:38:54 Searching Annoy index using 1 thread, search_k = 3000
16:38:54 Annoy recall = 100%
16:38:55 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
16:38:57 Initializing from normalized Laplacian + noise (using RSpectra)
16:38:57 Commencing optimization for 500 epochs, with 35796 positive edges
16:38:59 Optimization finished

a = DimPlot(samples.integrated, reduction = "umap")
b = FeaturePlot(samples.integrated, reduction = "umap", features = "Aif1")
b2 = FeaturePlot(samples.integrated, reduction = "umap", features = "Ptprc")
c = RidgePlot(samples.integrated, features = "Aif1")
c2 = RidgePlot(samples.integrated, features = "Ptprc")

d = DimPlot(samples.integrated, reduction = "umap", split.by = "Genotype_Treatment", pt.size = 1)
e = FeaturePlot(samples.integrated, reduction = "umap", features = "Aif1", split.by = "Genotype_Treatment")
e2 = FeaturePlot(samples.integrated, reduction = "umap", features = "Ptprc", split.by = "Genotype_Treatment")

a|b|b2

c|c2

Picking joint bandwidth of 0.194

Picking joint bandwidth of 0.352

getwd() # checks where you are run it directly in the console not here 

[1] "/files/scSeq_Hefendehl"

ggsave("/files/scSeq_Hefendehl/output/cluster_desiree_orginal.pdf", a)

Saving 8 x 4 in image

d

e

e2

find markers for the Aif1 neg

exclude cells with low Aif1 expression and X or Y coded genes

Exclusion and data harmonization step

samples.integrated.filt<- samples.integrated

# no filtering of Aif1 or Ptpcr uncomment section if you want to filter 
#samples.integrated.filt <- subset(x = samples.integrated.filt, subset = Aif1 > 0)
#samples.integrated.filt <- subset(x = samples.integrated.filt, subset = Ptprc > 0)


sex.genes <- rownames(samples.integrated.filt) %in%  annotations$gene_name[grepl("X|Y", annotations$seq_name)]
Gmgenes <- grepl("^Gm", rownames(samples.integrated.filt))
Rikgenes <- grepl(".*Rik$", rownames(samples.integrated.filt))

samples.integrated.filt<- samples.integrated.filt[!(sex.genes|Gmgenes|Rikgenes),]


DefaultAssay(samples.integrated.filt)<- "RNA"

ifnb.list <- SplitObject(samples.integrated, split.by = "Plate")
ifnb.list <- lapply(X = ifnb.list, FUN = function(x) {
  x <- NormalizeData(x)
  x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})

features <- SelectIntegrationFeatures(object.list = ifnb.list)

feat.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features)

Scaling features for provided objects

Finding all pairwise anchors

Running CCA

Merging objects

Finding neighborhoods

Finding anchors

    Found 814 anchors

Filtering anchors

Warning in FilterAnchors(object = object.pair, assay = assay, slot = slot, :
Number of anchor cells is less than k.filter. Retaining all anchors.

Running CCA

Merging objects

Finding neighborhoods

Finding anchors

    Found 941 anchors

Filtering anchors

    Retained 895 anchors

Running CCA

Merging objects

Finding neighborhoods

Finding anchors

    Found 841 anchors

Filtering anchors

Warning in FilterAnchors(object = object.pair, assay = assay, slot = slot, :
Number of anchor cells is less than k.filter. Retaining all anchors.

Running CCA

Merging objects

Finding neighborhoods

Finding anchors

    Found 927 anchors

Filtering anchors

    Retained 849 anchors

Running CCA

Merging objects

Finding neighborhoods

Finding anchors

    Found 832 anchors

Filtering anchors

Warning in FilterAnchors(object = object.pair, assay = assay, slot = slot, :
Number of anchor cells is less than k.filter. Retaining all anchors.

Running CCA

Merging objects

Finding neighborhoods

Finding anchors

    Found 958 anchors

Filtering anchors

    Retained 869 anchors

samples.integrated <- IntegrateData(anchorset = feat.anchors)

Merging dataset 2 into 3

Extracting anchors for merged samples

Finding integration vectors

Finding integration vector weights

Integrating data

Merging dataset 1 into 3 2

Extracting anchors for merged samples

Finding integration vectors

Finding integration vector weights

Integrating data

Merging dataset 4 into 3 2 1

Extracting anchors for merged samples

Finding integration vectors

Finding integration vector weights

Integrating data

samples.integrated.filt <- SCTransform(samples.integrated.filt, ncells = 3000)

Running SCTransform on assay: RNA

vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.

Calculating cell attributes from input UMI matrix: log_umi

Variance stabilizing transformation of count matrix of size 17541 by 905

Model formula is y ~ log_umi

Get Negative Binomial regression parameters per gene

Using 2000 genes, 905 cells

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Second step: Get residuals using fitted parameters for 17541 genes

Computing corrected count matrix for 17541 genes

Calculating gene attributes

Wall clock passed: Time difference of 6.410278 secs

Determine variable features

Centering data matrix

Place corrected count matrix in counts slot

Warning: Different cells and/or features from existing assay SCT

Set default assay to SCT

samples.integrated.filt <- ScaleData(object = samples.integrated.filt)

Centering and scaling data matrix

samples.integrated.filt <- FindVariableFeatures(samples.integrated.filt)
samples.integrated.filt <- RunPCA(samples.integrated.filt, npcs = 50)

PC_ 1 
Positive:  Vmn1r13, Olfr907, Rfx7, Pde6a, Pecam1, AC164437.4, Slc6a20b, Eva1c, Cux2, Slc39a2 
       Sass6, AC121793.2, Lipe, CT009480.1, Klkb1, AC162938.1, Cep97, Mcc, Tnfrsf14, AC153506.3 
       AC125532.1, AC121793.1, Galnt17, Srd5a3, Dock10, Gabra2, Pi4kb, AC127302.1, Rbm4b, Eif2a 
Negative:  Ctsd, Fth1, Ftl1, Lyz2, AY036118, Apoe, Lgals3bp, mt-Co3, mt-Atp6, Lag3 
       Gapdh, Cst7, mt-Nd1, P2ry13, Cd14, Tmem176b, mt-Rnr2, Csf3r, Rack1, Cd83 
       Ccl6, Actg1, Clec7a, Ank, Mdh2, Ighm, mt-Co2, Tcap, Axl, Tnfrsf12a 
PC_ 2 
Positive:  S100a8, Ifitm2, S100a11, S100a9, Anxa2, S100a6, Ftl1, Mcemp1, Gsr, Slfn1 
       Gsn, Il1r2, Lilrb4a, Mmp9, Ifitm1, Cd44, Clec4d, Fxyd5, Lsp1, Anxa1 
       Adgre5, Lilr4b, Wfdc17, Cdk2ap2, Hp, Zyx, Plbd1, Fth1, Fgr, Lgals3 
Negative:  Olfr1307, AC122850.1, CT010444.1, Olfr635, AC153522.1, AC164629.1, AC127302.1, Olfr46, D10Wsu102e, AC132221.1 
       AC158633.2, AC125532.1, Kcnq1ot1, AC159625.1, AC162934.3, AC153887.2, AC153532.1, Olfr1336, AC161254.1, AC152979.2 
       CT025624.1, AC164629.5, AC157654.1, AC102369.2, AC160336.1, Hsd3b2, AC154517.2, AC153950.1, AC164629.4, AC174797.1 
PC_ 3 
Positive:  S100a8, S100a9, S100a11, Cd24a, Slpi, Lrg1, Mmp9, Cxcr2, S100a6, Mcemp1 
       Hp, Ifitm1, Slfn1, Clec4d, Retnlg, Sell, Iqgap1, Pglyrp1, Il1r2, Adgre5 
       Cd44, Fxyd5, Gda, Gsr, Ifitm2, Sirpb1b, Slfn4, Clec4e, Wfdc21, Sirpb1c 
Negative:  Bhlhe41, P2ry13, Ctsd, Tanc2, Lag3, Mrpl23-ps1, Tspan14, Tmem176b, Cd14, Tmem206 
       AC149090.1, Cryl1, Foxn3, Lgals3bp, Mgat4a, Cd83, Gnaz, Gpr84, mt-Nd1, St6galnac4 
       Etv5, Pik3r1, Phgdh, Trmt2a, mt-Co3, Nop56, Kcnk13, Pcna, Rrp7a, Slc46a1 
PC_ 4 
Positive:  Cst7, Apoe, Cd74, Axl, Lgals3bp, Clec7a, Lpl, Lyz2, mt-Nd1, Ank 
       Capg, H2-Ab1, Gapdh, mt-Co3, Ftl1, Itgax, Ccl3, AY036118, Ifitm3, H2-Aa 
       Ccl6, Rack1, S100a4, mt-Nd5, Ch25h, Aplp2, Myo5a, H2-Eb1, Tpi1, Lgals1 
Negative:  AC153506.3, Gnaz, CT009480.1, Olfr907, Cep97, Slc6a20b, Pecam1, Mrpl23-ps1, AC162938.1, Tug1 
       Slc39a2, Dock10, Sass6, Bmt2, Klkb1, Pde6a, Eva1c, Rbm4b, Tulp1, Lipe 
       Bhlhe41, Tnfrsf14, Galnt17, Kdsr, AC166060.1, Cux2, AC164437.4, Mrpl19, AC161763.1, Eif2a 
PC_ 5 
Positive:  P2ry13, Tanc2, Actg1, AC124562.2, Mgat4a, Nfam1, AC149090.1, AC160130.1, Pik3r1, Tspan14 
       AC152414.1, Gnaz, Snx29, AC161250.2, Zfp638, BC035044, Csf3r, Etv5, Zfp292, Ccnd2 
       Zcchc7, Mtmr6, Kif21b, Mrpl23-ps1, Hjurp, Nfkbia, AC165106.3, Tgm2, Tlr6, Iffo1 
Negative:  AC121793.2, Mcc, Tnfrsf14, Apoe, Pi4kb, Slc39a2, AC161763.1, AC164437.4, Klkb1, Lipe 
       Cux2, Cst7, Eva1c, Tcf7l2, Sass6, Mrpl19, Pecam1, Cbx1, Atp1a2, Pde6a 
       Eif2a, AC162938.1, Clec7a, Axl, Rfx7, Kdsr, Asl, Rbm4b, Slc6a20b, Galnt17 

samples.integrated.filt <- RunHarmony(samples.integrated.filt, group.by.vars="Plate")

Transposing data matrix

Initializing state using k-means centroids initialization

Harmony 1/10

Harmony 2/10

Harmony 3/10

Harmony 4/10

Harmony 5/10

Harmony 6/10

Harmony 7/10

Harmony 8/10

Harmony 9/10

Harmony 10/10

p <- ElbowPlot(samples.integrated.filt, ndims = 50, reduction = "harmony")
p

ggsave(filename = "/files/scSeq_Hefendehl/output/Ellbowplot.svg", p)

Saving 4 x 4 in image

Re-optimize clustering

large difference in cell distribution between the genotypes

reslist= seq(0.2,1.2,0.2)

DefaultAssay(samples.integrated.filt)<- "SCT"
samples.integrated.filt.target <- RunUMAP(samples.integrated.filt, reduction = "pca",  features = cluster.genes) %>% 
  FindNeighbors(features=cluster.genes) %>% FindClusters(features=cluster.genes, resolution = reslist)

16:40:09 UMAP embedding parameters a = 0.9922 b = 1.112

Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'

Also defined by 'spam'

16:40:09 Read 905 rows and found 30 numeric columns

16:40:09 Using Annoy for neighbor search, n_neighbors = 30

Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'

Also defined by 'spam'

16:40:09 Building Annoy index with metric = cosine, n_trees = 50

0%   10   20   30   40   50   60   70   80   90   100%

[----|----|----|----|----|----|----|----|----|----|

**************************************************|
16:40:09 Writing NN index file to temp file /tmp/RtmpdRmKoZ/filef8276eb07e7
16:40:09 Searching Annoy index using 1 thread, search_k = 3000
16:40:09 Annoy recall = 100%
16:40:10 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
16:40:12 Initializing from normalized Laplacian + noise (using RSpectra)
16:40:12 Commencing optimization for 500 epochs, with 41882 positive edges
16:40:14 Optimization finished
Computing nearest neighbor graph
Computing SNN

Warning: The following arguments are not used: features

Warning: The following arguments are not used: features

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 905
Number of edges: 27460

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8927
Number of communities: 6
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 905
Number of edges: 27460

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8497
Number of communities: 8
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 905
Number of edges: 27460

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8179
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 905
Number of edges: 27460

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7891
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 905
Number of edges: 27460

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7607
Number of communities: 10
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 905
Number of edges: 27460

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7372
Number of communities: 10
Elapsed time: 0 seconds

library(clustree)
clustreeplot <- clustree(samples.integrated.filt.target, prefix = "SCT_snn_res.")
clustreeplot

Idents(samples.integrated.filt.target) = "SCT_snn_res.0.6"
a<- DimPlot(samples.integrated.filt.target)
b <- DimPlot(samples.integrated.filt.target, split.by = "Genotype_Treatment")
c<- FeaturePlot(samples.integrated.filt.target,features = homeostat.genes)

a

b

c

ggsave(filename = "/files/scSeq_Hefendehl/output/Gene_list_based_Clustertree.svg", clustreeplot)

Saving 8 x 8 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/Gene_list_based_Clusters.svg", a)

Saving 8 x 8 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/Gene_list_based_Clusters_byCondition.svg", b)

Saving 8 x 8 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/Gene_list_based_Clusters_byHomeostatgenes.svg", c)

Saving 8 x 8 in image

All gegenes based clustering

reslist= seq(0.2,1.2,0.2)


DefaultAssay(samples.integrated.filt)<- "SCT"
samples.integrated.filt <- RunUMAP(samples.integrated.filt, reduction = "pca", dims=1:15) %>% 
  FindNeighbors() %>% FindClusters(resolution = reslist)

16:40:21 UMAP embedding parameters a = 0.9922 b = 1.112

Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'

Also defined by 'spam'

16:40:21 Read 905 rows and found 15 numeric columns

16:40:21 Using Annoy for neighbor search, n_neighbors = 30

Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'

Also defined by 'spam'

16:40:21 Building Annoy index with metric = cosine, n_trees = 50

0%   10   20   30   40   50   60   70   80   90   100%

[----|----|----|----|----|----|----|----|----|----|

**************************************************|
16:40:21 Writing NN index file to temp file /tmp/RtmpdRmKoZ/filef8222af6283
16:40:21 Searching Annoy index using 1 thread, search_k = 3000
16:40:22 Annoy recall = 100%
16:40:23 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
16:40:25 Initializing from normalized Laplacian + noise (using RSpectra)
16:40:25 Commencing optimization for 500 epochs, with 35140 positive edges
16:40:27 Optimization finished
Computing nearest neighbor graph
Computing SNN

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 905
Number of edges: 27460

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8927
Number of communities: 6
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 905
Number of edges: 27460

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8497
Number of communities: 8
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 905
Number of edges: 27460

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8179
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 905
Number of edges: 27460

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7891
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 905
Number of edges: 27460

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7607
Number of communities: 10
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 905
Number of edges: 27460

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7372
Number of communities: 10
Elapsed time: 0 seconds

clustreeplot<-clustree(samples.integrated.filt, prefix = "SCT_snn_res.")

Idents(samples.integrated.filt) = "SCT_snn_res.0.6"
a<-DimPlot(samples.integrated.filt)
b<-DimPlot(samples.integrated.filt, split.by = "Genotype_Treatment")
c<-FeaturePlot(samples.integrated.filt,features = homeostat.genes)

clustreeplot

a

b

c

ggsave(filename = "/files/scSeq_Hefendehl/output/All_genes_Clustertree.svg", clustreeplot)

Saving 7 x 5 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/All_genes_Clusters.svg", a)

Saving 7 x 5 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/All_genes_Clusters_byCondition.svg", b)

Saving 7 x 5 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/All_genes_Clusters_byHomeostatgenes.svg", c)

Saving 7 x 5 in image

samples.integrated.filt <- RunUMAP(samples.integrated.filt, reduction = "pca", dims=1:15) %>% 
  FindNeighbors() %>% FindClusters(resolution = 0.6)

16:40:33 UMAP embedding parameters a = 0.9922 b = 1.112

Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'

Also defined by 'spam'

16:40:33 Read 905 rows and found 15 numeric columns

16:40:33 Using Annoy for neighbor search, n_neighbors = 30

Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'

Also defined by 'spam'

16:40:33 Building Annoy index with metric = cosine, n_trees = 50

0%   10   20   30   40   50   60   70   80   90   100%

[----|----|----|----|----|----|----|----|----|----|

**************************************************|
16:40:34 Writing NN index file to temp file /tmp/RtmpdRmKoZ/filef821612c0ba
16:40:34 Searching Annoy index using 1 thread, search_k = 3000
16:40:34 Annoy recall = 100%
16:40:35 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
16:40:37 Initializing from normalized Laplacian + noise (using RSpectra)
16:40:37 Commencing optimization for 500 epochs, with 35140 positive edges
16:40:39 Optimization finished
Computing nearest neighbor graph
Computing SNN

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 905
Number of edges: 27460

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8179
Number of communities: 9
Elapsed time: 0 seconds

a<-DimPlot(samples.integrated.filt, group.by = "seurat_clusters", pt.size =1, label = T) + NoLegend()
b<-DimPlot(samples.integrated.filt, reduction = "umap", split.by = "Plate", pt.size = 1)
c<-DimPlot(samples.integrated.filt, reduction = "umap", split.by = "Genotype_Treatment", pt.size =1)

a

b

c

ggsave(filename = "/files/scSeq_Hefendehl/output/Opt_All_genes_all_cells_Clusters.svg", a)

Saving 7 x 5 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/Opt_All_genes_all_cells_Clusters_byPlate.svg", b)

Saving 7 x 5 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/Opt_All_genes_all_cells_Clusters_byCondition.svg", c)

Saving 7 x 5 in image

Cell cycle scoring

cell_cycle_markers <- dplyr::left_join(cell_cycle_genes, annotations, by = c("geneID" = "gene_id"))

# Acquire the S phase genes
s_genes <- cell_cycle_markers %>%
  dplyr::filter(phase == "S") %>%
  pull("gene_name")

# Acquire the G2M phase genes        
g2m_genes <- cell_cycle_markers %>%
  dplyr::filter(phase == "G2/M") %>%
  pull("gene_name")

samples.integrated.filt<-CellCycleScoring(samples.integrated.filt, s.features = s_genes, g2m.features = g2m_genes)

Warning: The following features are not present in the object: Pola1, Gmnn, not
searching for symbol synonyms

Warning: The following features are not present in the object: Gtse1, not
searching for symbol synonyms

a<-DimPlot(samples.integrated.filt, reduction = "umap", group.by = "Phase", pt.size =1)
b<-DimPlot(samples.integrated.filt, reduction = "umap", group.by = "Phase", split.by = "Plate", pt.size =1)
c<-DimPlot(samples.integrated.filt, reduction = "umap", group.by = "Phase", split.by = "Genotype_Treatment", pt.size =1)

a

b

c

ggsave(filename = "/files/scSeq_Hefendehl/output/G2M_Opt_All_genes_all_cells_Clusters.svg", a)

Saving 7 x 5 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/G2M_Opt_All_genes_all_cells_Clusters_byPlate.svg", b)

Saving 7 x 5 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/G2M_Opt_All_genes_all_cells_Clusters_byCondition.svg", c)

Saving 7 x 5 in image

map cell types

ref.sce.mm <- celldex::MouseRNAseqData()

snapshotDate(): 2022-10-31

see ?celldex and browseVignettes('celldex') for documentation

loading from cache

see ?celldex and browseVignettes('celldex') for documentation

loading from cache

sce.filt<- as.SingleCellExperiment(samples.integrated.filt, assay = "SCT")

pred.mouseRNA <- SingleR(test =sce.filt, ref = ref.sce.mm, assay.type.test=1,
                         labels = ref.sce.mm$label.fine)

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

table(pred.mouseRNA$labels) #fine tuned cell types

              aNSCs           Ependymal        Granulocytes         Macrophages 
                  1                   1                  25                   6 
          Microglia Microglia activated           Monocytes   Neurons activated 
                852                   5                  10                   1 
           NK cells             T cells 
                  1                   3 

#table(pred.mouseRNA$pruned.labels) #cell type after pruning

samples.integrated.filt$Immgen_sc_labels_agc <- NA
samples.integrated.filt$Immgen_sc_labels_agc <- pred.mouseRNA$labels


plotScoreHeatmap(pred.mouseRNA, show.labels = TRUE,
                 annotation_col=data.frame(cluster=samples.integrated.filt$seurat_clusters,
                                           
                                           row.names=rownames(pred.mouseRNA)))

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Idents(samples.integrated.filt)<- "seurat_clusters"

a<- DimPlot(samples.integrated.filt, reduction="umap", group.by="Immgen_sc_labels_agc", 
        label = F)
b<- DimPlot(samples.integrated.filt, reduction="umap", group.by="Immgen_sc_labels_agc", 
        label = F, split.by = "Plate")
c<- DimPlot(samples.integrated.filt, reduction="umap", group.by="Immgen_sc_labels_agc", split.by = "Genotype_Treatment",
        label = F)

a

b

c

ggsave(filename = "/files/scSeq_Hefendehl/output/Celltypes_Opt_All_genes_all_cells_Clusters.svg", a)

Saving 7 x 5 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/Celltypes_Opt_All_genes_all_cells_Clusters_byPlate.svg", b)

Saving 7 x 5 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/Celltypes_Opt_All_genes_all_cells_Clusters_byCondition.svg", c)

Saving 7 x 5 in image

filter and recluster only Microglia

samples.integrated.filt = samples.integrated.filt[,samples.integrated.filt$Immgen_sc_labels_agc=="Microglia"]

samples.integrated.filt <- RunUMAP(samples.integrated.filt, reduction = "pca", dims=1:15) %>% 
  FindNeighbors() %>% FindClusters(resolution = 0.6)

16:41:02 UMAP embedding parameters a = 0.9922 b = 1.112

Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'

Also defined by 'spam'

16:41:02 Read 852 rows and found 15 numeric columns

16:41:02 Using Annoy for neighbor search, n_neighbors = 30

Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'

Also defined by 'spam'

16:41:02 Building Annoy index with metric = cosine, n_trees = 50

0%   10   20   30   40   50   60   70   80   90   100%

[----|----|----|----|----|----|----|----|----|----|

**************************************************|
16:41:02 Writing NN index file to temp file /tmp/RtmpdRmKoZ/filef82504bb75c
16:41:02 Searching Annoy index using 1 thread, search_k = 3000
16:41:02 Annoy recall = 100%
16:41:03 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
16:41:05 Initializing from normalized Laplacian + noise (using RSpectra)
16:41:05 Commencing optimization for 500 epochs, with 33398 positive edges
16:41:07 Optimization finished
Computing nearest neighbor graph
Computing SNN

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 852
Number of edges: 26537

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8097
Number of communities: 7
Elapsed time: 0 seconds

n = length(unique(samples.integrated.filt$seurat_clusters))
samples.integrated.filt$labels<- factor(Idents(samples.integrated.filt), 
                                        levels = c(0:(n-1)), labels=paste0("Microglia", 0:(n-1))
                                            )

Idents(samples.integrated.filt) = "labels"
a <- DimPlot(samples.integrated.filt, reduction="umap", label = T)
b <- DimPlot(samples.integrated.filt, reduction="umap", split.by = "Plate", label = F)
c <- DimPlot(samples.integrated.filt, reduction="umap", split.by = "Genotype_Treatment", label = F)
d <- DimPlot(samples.integrated.filt, reduction = "umap", group.by = "Genotype_Treatment")

a

b

c

d

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Opt_All_genes_Clusters.svg", a)

Saving 7 x 5 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Opt_All_genes_Clusters_byPlate.svg", b)

Saving 7 x 5 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Opt_All_genes_Clusters_byCondition.svg", c)

Saving 7 x 5 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Opt_All_genes_Condition.svg", d)

Saving 7 x 5 in image

Markers for Clusters

all clusters

Idents(samples.integrated.filt) <- "labels"
Markers=FindAllMarkers(samples.integrated.filt, logfc.threshold = 1.5)

Calculating cluster Microglia0

Calculating cluster Microglia1

Calculating cluster Microglia2

Calculating cluster Microglia3

Calculating cluster Microglia4

Calculating cluster Microglia5

Calculating cluster Microglia6

display_tab(Markers)

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

Markers %>% dplyr::select(c("cluster", "gene")) %>% split(., "cluster") -> gogenelist 
openxlsx::write.xlsx(Markers, "./output/Clusters_Markers_all.xlsx")
geneunivers<-rownames(samples.integrated.filt)
GOterm=getGOresults(gogenelist, genereference = geneunivers, organism = "mmusculus")

Loading required package: gprofiler2

display_tab(GOterm$result)

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

openxlsx::write.xlsx(GOterm$result, "./output/Clusters_GOterms_Markers_all.xlsx")

Warning in wb$writeData(df = x, colNames = colNames, sheet = sheet, startCol = startCol, : P ISS ISO IEA,IM ... ,IMP,IBA,IMP ISO is truncated. 
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Number of characters exeed the limit of 32767.

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Number of characters exeed the limit of 32767.

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Number of characters exeed the limit of 32767.

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Number of characters exeed the limit of 32767.

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Number of characters exeed the limit of 32767.

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Number of characters exeed the limit of 32767.

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Number of characters exeed the limit of 32767.

Warning in wb$writeData(df = x, colNames = colNames, sheet = sheet, startCol = startCol, : ,Maea,Nipsnap3b, ... 5,Gorasp1,Dhrs7c is truncated. 
Number of characters exeed the limit of 32767.

DEG Microglia 2 vs Microglia 4

Idents(samples.integrated.filt) <- "labels"
Markers2vs4=FindMarkers(samples.integrated.filt, 
                                  ident.1 = "Microglia2", ident.2 = "Microglia4", 
                                  test.use = "LR", logfc.threshold = 1.5)

Warning: The following arguments are not used: norm.method

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display_tab(Markers2vs4)

openxlsx::write.xlsx(Markers2vs4, "./output/Clusters_DEG_Micorglia2vs4.xlsx")
GOterm=getGOresults(list(Micorglia2vs4=rownames(Markers2vs4)[Markers2vs4$p_val_adj<0.05]), genereference = geneunivers, organism = "mmusculus")
display_tab(GOterm$result)

openxlsx::write.xlsx(GOterm$result, "./output/Clusters_GOterms_DEG_Micorglia2vs4.xlsx")

DEG Microglia 5 vs Microglia 6

Idents(samples.integrated.filt) <- "labels"
Micorglia5vs6=FindMarkers(samples.integrated.filt, 
                                  ident.1 = "Microglia5", 
                       ident.2 = "Microglia6", 
                                  test.use = "LR", logfc.threshold = 1.5)

Warning: The following arguments are not used: norm.method

Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

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display_tab(Micorglia5vs6)

openxlsx::write.xlsx(Micorglia5vs6, "./output/Clusters_DEG_Markers2vs6.xlsx")
GOterm=getGOresults(geneset =list(Micorglia5vs6=rownames(Micorglia5vs6)[Micorglia5vs6$p_val_adj<0.05]), 
                    genereference = geneunivers, organism = "mmusculus")
display_tab(GOterm$result)

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openxlsx::write.xlsx(GOterm$result, "./output/Clusters_GOterms_DEG_Micorglia2vs6.xlsx")

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Comparison of DEGs in DAMs between treatments/genotypes

5. Subcluster overlaps

We will provide gene lists for different previously described microglia subclusters including homeostatic, DAM 1/DAM2 and IRM identification list. Please generate a Venn diagram or similar to see how big the overlap of our cells of interest (identified in the scDataset via the spatial transcriptomics list) is with the individual subclusters,

define DAM

Profiles based on genelist provided by Jan Hoffmann

NOT done: Alterantive DAM profiles based on Cell. 2017 Jun 15;169(7):1276-1290.e17. doi: 10.1016/j.cell.2017.05.018)

DAM1

#load("./data/DAM_genelists.RData")
DefaultAssay(samples.integrated.filt) = "SCT"

samples.integrated.filt.target<- AddModuleScore(object = samples.integrated.filt.target, 
                                          features = list(DAM1_up), name = "DAM_score")

#FeaturePlot(samples.integrated.filt.target, features = "DAM_score1")+scale_color_viridis_c()

samples.integrated.filt <- AddModuleScore(object = samples.integrated.filt, 
                                          features = list(DAM_up), name = "DAM_score")

p4 <- FeaturePlot(object = samples.integrated.filt, features = "DAM_score1")+scale_color_viridis_c()

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

p4a <- DimPlot(object = samples.integrated.filt, reduction = "umap")

DAMcutoff = 1.5
phist = ggplot(data = samples.integrated.filt@meta.data, aes(x=DAM_score1))+
  geom_density()+geom_vline(xintercept = DAMcutoff)+
  theme_classic()

samples.integrated.filt$DAM_binary = as.factor(samples.integrated.filt$DAM_score1>=DAMcutoff)
pbin <- DimPlot(object = samples.integrated.filt, group.by =  "DAM_binary")

comb = phist | p4 | pbin| p4a
comb

prop.table(table(DAMs=samples.integrated.filt$DAM_binary, Clusters=samples.integrated.filt$labels), margin = 2)

       Clusters
DAMs    Microglia0 Microglia1 Microglia2 Microglia3 Microglia4 Microglia5
  FALSE 0.01415094 0.73863636 1.00000000 0.66086957 0.97938144 0.94594595
  TRUE  0.98584906 0.26136364 0.00000000 0.33913043 0.02061856 0.05405405
       Clusters
DAMs    Microglia6
  FALSE 0.89655172
  TRUE  0.10344828

dam_GT_p <-DimPlot(object = samples.integrated.filt, group.by =  "DAM_binary", split.by = "Genotype_Treatment")


ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_DAM1_calling.svg", comb)

Saving 12 x 4 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_DAM1_bygenotype.svg", dam_GT_p)

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phist

p4

pbin

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_DAM1_thresholding.svg", phist)

Saving 7 x 5 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_DAM1_scoring.svg", p4)

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ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_DAM1_binary.svg", pbin)

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samples.integrated.filt <- AddModuleScore(object = samples.integrated.filt, 
                                          features = list(DAM2_up), name = "DAM2_score")

Warning: The following features are not present in the object: Lilrb4, not
searching for symbol synonyms

p4 <- FeaturePlot(object = samples.integrated.filt, features = "DAM2_score1")+scale_color_viridis_c()

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

p4a <- DimPlot(object = samples.integrated.filt, reduction = "umap")

DAM2cutoff = 1.0
phist = ggplot(data = samples.integrated.filt@meta.data, aes(x=DAM2_score1))+
  geom_density()+geom_vline(xintercept = DAM2cutoff)+
  theme_classic()

samples.integrated.filt$DAM2_binary = as.factor(samples.integrated.filt$DAM2_score1>=DAM2cutoff)
pbin <- DimPlot(object = samples.integrated.filt, group.by =  "DAM2_binary")
comb = phist | p4 | pbin| p4a


dam_GT_p <-DimPlot(object = samples.integrated.filt, group.by =  "DAM2_binary", split.by = "Genotype_Treatment")

comb

dam_GT_p

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_DAM2_calling.svg", comb)

Saving 12 x 4 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_DAM2_bygenotype.svg", dam_GT_p)

Saving 12 x 4 in image

prop.table(table(DAM2=samples.integrated.filt$DAM2_binary, Clusters=samples.integrated.filt$labels), margin = 2)

       Clusters
DAM2     Microglia0  Microglia1  Microglia2  Microglia3  Microglia4  Microglia5
  FALSE 0.108490566 0.914772727 0.991666667 0.834782609 0.989690722 0.945945946
  TRUE  0.891509434 0.085227273 0.008333333 0.165217391 0.010309278 0.054054054
       Clusters
DAM2     Microglia6
  FALSE 0.913793103
  TRUE  0.086206897

prop.table(table(DAM2=samples.integrated.filt$DAM2_binary, DAMs=samples.integrated.filt$DAM_binary), margin = 2)

       DAMs
DAM2          FALSE        TRUE
  FALSE 0.994505495 0.245098039
  TRUE  0.005494505 0.754901961

a <- ggplot(samples.integrated.filt@meta.data, aes(x=DAM2_score1, y=DAM_score1))+
  geom_density_2d_filled()+geom_point(col="#FFFFFF55")+theme_classic()+ggtitle("all cells")

b<- ggplot(samples.integrated.filt@meta.data[samples.integrated.filt$DAM_binary==TRUE,], aes(x=DAM2_score1, y=DAM_score1))+geom_density_2d_filled()+geom_point(col="#FFFFFF55")+theme_classic()+ggtitle("DAMs only")

combo <- a|b
combo

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_DAMvsDAM2_contourplot.svg", combo)

Saving 12 x 5 in image

proportion tables show percebtages of DAM cells per condition

metadata=samples.integrated.filt@meta.data
restab = table(GT_Treat = metadata$Genotype_Treatment, DAMs=metadata$DAM_binary)
restab

               DAMs
GT_Treat        FALSE TRUE
  WT_Ctrl         235    1
  WT_Stroke        65   61
  APPPS1_Ctrl     134  169
  APPPS1_Stroke   112   75

prop.table(restab, margin = 1)

               DAMs
GT_Treat              FALSE        TRUE
  WT_Ctrl       0.995762712 0.004237288
  WT_Stroke     0.515873016 0.484126984
  APPPS1_Ctrl   0.442244224 0.557755776
  APPPS1_Stroke 0.598930481 0.401069519

chisq.test(restab)

    Pearson's Chi-squared test

data:  restab
X-squared = 191.06, df = 3, p-value < 2.2e-16

proplot <- ggplot(as.data.frame(restab), 
       aes(fill=DAMs, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()+ggtitle("DAM proportions")

proplot

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_DAMproportions_per_condition_all.svg", proplot)

Saving 7 x 5 in image

library(chisq.posthoc.test)
chisq.posthoc.test(restab)

      Dimension     Value     FALSE       TRUE
1       WT_Ctrl Residuals 13.365819 -13.365819
2       WT_Ctrl  p values  0.000000   0.000000
3     WT_Stroke Residuals -3.167614   3.167614
4     WT_Stroke  p values  0.012296   0.012296
5   APPPS1_Ctrl Residuals -8.976739   8.976739
6   APPPS1_Ctrl  p values  0.000000   0.000000
7 APPPS1_Stroke Residuals -1.352314   1.352314
8 APPPS1_Stroke  p values  1.000000   1.000000

#To Do Pairwise 

a) DAMs in APPPS1 vs. DAMs in APPPS1 + Stroke – Venn diagram, or similar

metadata=samples.integrated.filt@meta.data[samples.integrated.filt$Genotype == "APPPS1", ]
restab = table(GT_Treat = as.character(metadata$Genotype_Treatment), DAMs=metadata$DAM_binary)
restab

               DAMs
GT_Treat        FALSE TRUE
  APPPS1_Ctrl     134  169
  APPPS1_Stroke   112   75

prop.table(restab, margin = 1)

               DAMs
GT_Treat            FALSE      TRUE
  APPPS1_Ctrl   0.4422442 0.5577558
  APPPS1_Stroke 0.5989305 0.4010695

fisher.test(restab)

    Fisher's Exact Test for Count Data

data:  restab
p-value = 0.0008179
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.3602999 0.7815525
sample estimates:
odds ratio 
 0.5316483 

proplot<-ggplot(as.data.frame(restab), 
       aes(fill=DAMs, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()

proplot

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_DAMproportions_per_condition_APPPS1.svg", proplot)

Saving 7 x 5 in image

b) DAMs in WT+Stroke vs. DAMs in APPPS1 + Stroke – Venn diagram, or similar

metadata=samples.integrated.filt@meta.data[samples.integrated.filt$Genotype == "WT", ]
restab = table(GT_Treat = as.character(metadata$Genotype_Treatment), DAMs=metadata$DAM_binary)
restab

           DAMs
GT_Treat    FALSE TRUE
  WT_Ctrl     235    1
  WT_Stroke    65   61

prop.table(restab, margin = 1)

           DAMs
GT_Treat          FALSE        TRUE
  WT_Ctrl   0.995762712 0.004237288
  WT_Stroke 0.515873016 0.484126984

fisher.test(restab)

    Fisher's Exact Test for Count Data

data:  restab
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
   36.09295 8376.89342
sample estimates:
odds ratio 
  216.2896 

proplot<-ggplot(as.data.frame(restab), 
       aes(fill=DAMs, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()

proplot

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_DAMproportions_per_condition_WT.svg", proplot)

Saving 7 x 5 in image

c) Separation of DAMs into DAM1 and DAM2 (with a provided gene list), repetition of a) and b) to see if our cells on interest overlap more with one or the other profile

metadata=samples.integrated.filt@meta.data[samples.integrated.filt$DAM_binary==TRUE,]
restab = table(GT_Treat = metadata$Genotype_Treatment, DAM2=metadata$DAM2_binary)
restab

               DAM2
GT_Treat        FALSE TRUE
  WT_Ctrl           0    1
  WT_Stroke        38   23
  APPPS1_Ctrl      15  154
  APPPS1_Stroke    22   53

prop.table(restab, margin = 1)

               DAM2
GT_Treat            FALSE      TRUE
  WT_Ctrl       0.0000000 1.0000000
  WT_Stroke     0.6229508 0.3770492
  APPPS1_Ctrl   0.0887574 0.9112426
  APPPS1_Stroke 0.2933333 0.7066667

chisq.test(restab)

Warning in chisq.test(restab): Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  restab
X-squared = 70.663, df = 3, p-value = 3.078e-15

proplot<-ggplot(as.data.frame(restab), 
       aes(fill=DAM2, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()

chisq.posthoc.test(restab)

Warning in chisq.test(x, ...): Chi-squared approximation may be incorrect

      Dimension     Value      FALSE       TRUE
1       WT_Ctrl Residuals -0.5707362  0.5707362
2       WT_Ctrl  p values  1.0000000  1.0000000
3     WT_Stroke Residuals  7.6674335 -7.6674335
4     WT_Stroke  p values  0.0000000  0.0000000
5   APPPS1_Ctrl Residuals -7.0615572  7.0615572
6   APPPS1_Ctrl  p values  0.0000000  0.0000000
7 APPPS1_Stroke Residuals  1.1177253 -1.1177253
8 APPPS1_Stroke  p values  1.0000000  1.0000000

proplot

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_DAM2proportionsofDAM1_per_condition.svg", proplot)

Saving 7 x 5 in image

metadata=samples.integrated.filt@meta.data[(samples.integrated.filt$DAM_binary==TRUE) & (samples.integrated.filt$Genotype=="APPPS1"),]
restab = table(GT_Treat = as.character(metadata$Genotype_Treatment), DAM2=metadata$DAM2_binary)
restab

               DAM2
GT_Treat        FALSE TRUE
  APPPS1_Ctrl      15  154
  APPPS1_Stroke    22   53

prop.table(restab, margin = 1)

               DAM2
GT_Treat            FALSE      TRUE
  APPPS1_Ctrl   0.0887574 0.9112426
  APPPS1_Stroke 0.2933333 0.7066667

chisq.test(restab)

    Pearson's Chi-squared test with Yates' continuity correction

data:  restab
X-squared = 15.347, df = 1, p-value = 8.948e-05

proplot<- ggplot(as.data.frame(restab), 
       aes(fill=DAM2, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()


proplot

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_DAM2proportionsofDAM1_per_condition_APPPS1.svg", proplot)

Saving 7 x 5 in image

metadata=samples.integrated.filt@meta.data[(samples.integrated.filt$DAM_binary==TRUE) & (samples.integrated.filt$Genotype=="WT"),]
restab = table(GT_Treat = as.character(metadata$Genotype_Treatment), DAM2=metadata$DAM2_binary)
restab

           DAM2
GT_Treat    FALSE TRUE
  WT_Ctrl       0    1
  WT_Stroke    38   23

prop.table(restab, margin = 1)

           DAM2
GT_Treat        FALSE      TRUE
  WT_Ctrl   0.0000000 1.0000000
  WT_Stroke 0.6229508 0.3770492

chisq.test(restab)

Warning in chisq.test(restab): Chi-squared approximation may be incorrect

    Pearson's Chi-squared test with Yates' continuity correction

data:  restab
X-squared = 0.054609, df = 1, p-value = 0.8152

proplot<-ggplot(as.data.frame(restab), 
       aes(fill=DAM2, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()


proplot

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_DAM2proportionsofDAM1_per_condition_WT.svg", proplot)

Saving 7 x 5 in image

Homeostatic Cells

# list from Jan Hofmann 
a <- RidgePlot(samples.integrated.filt, features = homeostat.genes, ncol = 2)
a

Picking joint bandwidth of 0.274

Picking joint bandwidth of 0.462

Picking joint bandwidth of 0.327

Picking joint bandwidth of 0.329

Picking joint bandwidth of 0.333

Picking joint bandwidth of 0.491

b<- FeaturePlot(samples.integrated.filt, features = homeostat.genes)

b

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_HomeostatGenes_Cluster_Ridgeplot.svg", a)

Saving 8 x 15 in image

Picking joint bandwidth of 0.274

Picking joint bandwidth of 0.462

Picking joint bandwidth of 0.327

Picking joint bandwidth of 0.329

Picking joint bandwidth of 0.333

Picking joint bandwidth of 0.491

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_HomeostatGenes_Featureplot.svg", a)

Saving 8 x 15 in image

Picking joint bandwidth of 0.274

Picking joint bandwidth of 0.462

Picking joint bandwidth of 0.327

Picking joint bandwidth of 0.329

Picking joint bandwidth of 0.333

Picking joint bandwidth of 0.491

samples.integrated.filt <- AddModuleScore(object = samples.integrated.filt, 
                                          features = list(homeostat.genes), name = "Homeo_score")

p4 <- FeaturePlot(object = samples.integrated.filt, features = "Homeo_score1")+scale_color_viridis_c()

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

p4a <- DimPlot(object = samples.integrated.filt, reduction = "umap")

Homeocutff = 2
phist = ggplot(data = samples.integrated.filt@meta.data, aes(x=Homeo_score1))+
  geom_density()+geom_vline(xintercept = Homeocutff)+theme_classic()

samples.integrated.filt$Homeo_binary = as.factor(samples.integrated.filt$Homeo_score1>=Homeocutff)
pbin <- DimPlot(object = samples.integrated.filt, group.by =  "Homeo_binary")
comb = phist | p4 | pbin| p4a

Hom_GT_p <-DimPlot(object = samples.integrated.filt, group.by =  "Homeo_binary", split.by = "Genotype_Treatment")

comb

Hom_GT_p

prop.table(table(Homeo=samples.integrated.filt$Homeo_binary, Clusters=samples.integrated.filt$labels), margin = 2)

       Clusters
Homeo   Microglia0 Microglia1 Microglia2 Microglia3 Microglia4 Microglia5
  FALSE 0.94339623 0.30113636 0.03333333 0.48695652 0.18556701 0.85135135
  TRUE  0.05660377 0.69886364 0.96666667 0.51304348 0.81443299 0.14864865
       Clusters
Homeo   Microglia6
  FALSE 0.96551724
  TRUE  0.03448276

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Homeo_calling.svg", comb)

Saving 12 x 4 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Homeo_bygenotype.svg", Hom_GT_p)

Saving 12 x 4 in image

phist

p4

pbin

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Homeo_thresholding.svg", phist)

Saving 7 x 5 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Homeo_scoring.svg", p4)

Saving 7 x 5 in image

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Homeo_binary.svg", pbin)

Saving 7 x 5 in image

Homeo vs DAM

metadata = samples.integrated.filt@meta.data

metadata$Homeo_DAM <- factor(1*as.logical(metadata$Homeo_binary) + 2*as.logical(metadata$DAM_binary), levels = c(0,1,2,3), labels = c("Neither", "Homeo", "DAM", "Unknown"))

samples.integrated.filt@meta.data <- metadata


a = DimPlot(samples.integrated.filt, reduction="umap", group.by = "Homeo_DAM")
a

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Homeo_DAM.svg", a)

Saving 7 x 5 in image

a <- ggplot(samples.integrated.filt@meta.data, aes(x=Homeo_score1, y=DAM_score1))+
  geom_density_2d_filled()+geom_point(col="#FFFFFF55")+theme_classic()+ggtitle("all cells")

b<- ggplot(samples.integrated.filt@meta.data[samples.integrated.filt$DAM_binary==TRUE,], aes(x=Homeo_score1, y=DAM_score1))+geom_density_2d_filled()+geom_point(col="#FFFFFF55")+theme_classic()+ggtitle("DAMs only")

a|b

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_DAMvsHomeo.svg", a|b)

Saving 12 x 5 in image

d) Homeostatic microglia (with provided gene list) in APPPS1 vs. Homeostatic microglia in APPPS1 + Stroke – Venn diagram, or similar

proportion tables show percentages of Homeostatic cells per condition

metadata=samples.integrated.filt@meta.data
restab = table(GT_Treat = metadata$Genotype_Treatment, Homeo=metadata$Homeo_binary)
restab

               Homeo
GT_Treat        FALSE TRUE
  WT_Ctrl          21  215
  WT_Stroke        78   48
  APPPS1_Ctrl     186  117
  APPPS1_Stroke   165   22

prop.table(restab, margin = 1)

               Homeo
GT_Treat             FALSE       TRUE
  WT_Ctrl       0.08898305 0.91101695
  WT_Stroke     0.61904762 0.38095238
  APPPS1_Ctrl   0.61386139 0.38613861
  APPPS1_Stroke 0.88235294 0.11764706

chisq.test(restab)

    Pearson's Chi-squared test

data:  restab
X-squared = 289.9, df = 3, p-value < 2.2e-16

proplot<-ggplot(as.data.frame(restab), 
       aes(fill=Homeo, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()


chisq.posthoc.test(restab)

      Dimension     Value      FALSE       TRUE
1       WT_Ctrl Residuals -15.894768  15.894768
2       WT_Ctrl  p values   0.000000   0.000000
3     WT_Stroke Residuals   2.213700  -2.213700
4     WT_Stroke  p values   0.214796   0.214796
5   APPPS1_Ctrl Residuals   3.722352  -3.722352
6   APPPS1_Ctrl  p values   0.001579   0.001579
7 APPPS1_Stroke Residuals  10.981937 -10.981937
8 APPPS1_Stroke  p values   0.000000   0.000000

proplot

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Homeoproportions_per_condition.svg", proplot)

Saving 7 x 5 in image

e) Homeostatic microglia in WT+Stroke vs. Homeostatic microglia in APPPS1 + Stroke – Venn diagram, or similar

metadata=samples.integrated.filt@meta.data[samples.integrated.filt$Genotype=="APPPS1",]
restab = table(GT_Treat = as.character(metadata$Genotype_Treatment), Homeo=metadata$Homeo_binary)
restab

               Homeo
GT_Treat        FALSE TRUE
  APPPS1_Ctrl     186  117
  APPPS1_Stroke   165   22

prop.table(restab, margin = 1)

               Homeo
GT_Treat            FALSE      TRUE
  APPPS1_Ctrl   0.6138614 0.3861386
  APPPS1_Stroke 0.8823529 0.1176471

chisq.test(restab)

    Pearson's Chi-squared test with Yates' continuity correction

data:  restab
X-squared = 39.712, df = 1, p-value = 2.944e-10

proplot<- ggplot(as.data.frame(restab), 
       aes(fill=Homeo, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()

proplot

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Homeoproportions_per_condition_APPPS1.svg", proplot)

Saving 7 x 5 in image

metadata=samples.integrated.filt@meta.data[samples.integrated.filt$Genotype=="WT",]
restab = table(GT_Treat = as.character(metadata$Genotype_Treatment), Homeo=metadata$Homeo_binary)
restab

           Homeo
GT_Treat    FALSE TRUE
  WT_Ctrl      21  215
  WT_Stroke    78   48

prop.table(restab, margin = 1)

           Homeo
GT_Treat         FALSE       TRUE
  WT_Ctrl   0.08898305 0.91101695
  WT_Stroke 0.61904762 0.38095238

chisq.test(restab)

    Pearson's Chi-squared test with Yates' continuity correction

data:  restab
X-squared = 113.51, df = 1, p-value < 2.2e-16

proplot<-ggplot(as.data.frame(restab), 
       aes(fill=Homeo, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()

proplot

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Homeoproportions_per_condition_WT.svg", proplot)

Saving 7 x 5 in image

### Spatial cell types 


Idents(samples.integrated.filt) = "Genotype_Treatment"
DoHeatmap(samples.integrated.filt, assay = "RNA", features = c(distal_genes, control_genes, Stroke_genes))+ggtitle("spatial genes all")

Warning in DoHeatmap(samples.integrated.filt, assay = "RNA", features =
c(distal_genes, : The following features were omitted as they were not found in
the scale.data slot for the RNA assay: Timp1, Tmsb4x, Gm14539, Cx3cl1, Gm15564,
Norad, Atp5md, Bex1, Nrsn1, H2az1, Prxl2b, Stmn3, Gng13

DoHeatmap(samples.integrated.filt, assay = "RNA",features = c(distal_genes))+ggtitle("spatial genes distal")

Warning in DoHeatmap(samples.integrated.filt, assay = "RNA", features =
c(distal_genes)): The following features were omitted as they were not found in
the scale.data slot for the RNA assay: Bex1, Nrsn1, H2az1, Prxl2b, Stmn3, Gng13

DoHeatmap(samples.integrated.filt, assay = "RNA",features = c(control_genes))+ggtitle("spatial genes control")

Warning in DoHeatmap(samples.integrated.filt, assay = "RNA", features =
c(control_genes)): The following features were omitted as they were not found
in the scale.data slot for the RNA assay: Gm14539, Cx3cl1, Gm15564, Norad,
Atp5md

DoHeatmap(samples.integrated.filt, assay = "RNA",features = c(Stroke_genes))+ggtitle("spatial genes peri-ictus")

Warning in DoHeatmap(samples.integrated.filt, assay = "RNA", features =
c(Stroke_genes)): The following features were omitted as they were not found in
the scale.data slot for the RNA assay: Timp1, Tmsb4x

DISTAL

#load("./data/DAM_genelists.RData")
DefaultAssay(samples.integrated.filt) = "SCT"

samples.integrated.filt <- AddModuleScore(object = samples.integrated.filt, 
                                          features = list(distal_genes), name = "Distal")

Warning: The following features are not present in the object: Gng13, Stmn3,
Prxl2b, H2az1, Ctxn1, Nrsn1, Bex1, Nrgn, Cck, not searching for symbol synonyms

p4 <- FeaturePlot(object = samples.integrated.filt, features = "Distal1")+scale_color_viridis_c()

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

p4a <- DimPlot(object = samples.integrated.filt, reduction = "umap")

Distalcutoff = 0.5
phist = ggplot(data = samples.integrated.filt@meta.data, aes(x=Distal1))+
  geom_density()+geom_vline(xintercept = Distalcutoff)+
  theme_classic()

samples.integrated.filt$Distal_binary = as.factor(samples.integrated.filt$Distal1>=Distalcutoff)
pbin <- DimPlot(object = samples.integrated.filt, group.by =  "Distal_binary")
phist | p4 | pbin| p4a

prop.table(table(Distals=samples.integrated.filt$Distal_binary, Clusters=samples.integrated.filt$labels), margin = 2)

       Clusters
Distals Microglia0 Microglia1 Microglia2 Microglia3 Microglia4 Microglia5
  FALSE 0.39150943 0.89204545 0.97500000 0.91304348 0.97938144 1.00000000
  TRUE  0.60849057 0.10795455 0.02500000 0.08695652 0.02061856 0.00000000
       Clusters
Distals Microglia6
  FALSE 1.00000000
  TRUE  0.00000000

DimPlot(object = samples.integrated.filt, group.by =  "Distal_binary", split.by = "Genotype_Treatment")

#load("./data/DAM_genelists.RData")
DefaultAssay(samples.integrated.filt) = "SCT"

samples.integrated.filt <- AddModuleScore(object = samples.integrated.filt, 
                                          features = list(control_genes), name = "Control")

Warning: The following features are not present in the object: Atp5md, Nrgn,
Norad, Dnm1, Gm15564, Cx3cl1, Gm14539, not searching for symbol synonyms

p4 <- FeaturePlot(object = samples.integrated.filt, features = "Control1")+scale_color_viridis_c()

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

p4a <- DimPlot(object = samples.integrated.filt, reduction = "umap")

Controlcutoff = 0.25
phist = ggplot(data = samples.integrated.filt@meta.data, aes(x=Control1))+
  geom_density()+geom_vline(xintercept = Controlcutoff)+
  theme_classic()

samples.integrated.filt$Control_binary = as.factor(samples.integrated.filt$Control1>=Controlcutoff)
pbin <- DimPlot(object = samples.integrated.filt, group.by =  "Control_binary")
phist | p4 | pbin| p4a

prop.table(table(Controls=samples.integrated.filt$Control_binary, Clusters=samples.integrated.filt$labels), margin = 2)

        Clusters
Controls Microglia0 Microglia1 Microglia2 Microglia3 Microglia4 Microglia5
   FALSE  0.7075472  0.6590909  0.5750000  0.7565217  0.5979381  0.8783784
   TRUE   0.2924528  0.3409091  0.4250000  0.2434783  0.4020619  0.1216216
        Clusters
Controls Microglia6
   FALSE  0.8965517
   TRUE   0.1034483

DimPlot(object = samples.integrated.filt, group.by =  "Control_binary", split.by = "Genotype_Treatment")

#load("./data/DAM_genelists.RData")
DefaultAssay(samples.integrated.filt) = "SCT"

samples.integrated.filt <- AddModuleScore(object = samples.integrated.filt, 
                                          features = list(Stroke_genes), name = "Peri_ictus")

Warning: The following features are not present in the object: Serpina3n,
Tmsb4x, Timp1, not searching for symbol synonyms

p4 <- FeaturePlot(object = samples.integrated.filt, features = "Peri_ictus1")+scale_color_viridis_c()

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

p4a <- DimPlot(object = samples.integrated.filt, reduction = "umap")

Strokecutoff = 2.5
phist = ggplot(data = samples.integrated.filt@meta.data, aes(x=Peri_ictus1))+
  geom_density()+geom_vline(xintercept = Strokecutoff)+
  theme_classic()+ggtitle("Peri_ictus")

samples.integrated.filt$Peri_ictus_binary = as.factor(samples.integrated.filt$Peri_ictus1>=Strokecutoff)
pbin <- DimPlot(object = samples.integrated.filt, group.by =  "Peri_ictus_binary")
phist | p4 | pbin| p4a

prop.table(table(Strokes=samples.integrated.filt$Peri_ictus_binary, Clusters=samples.integrated.filt$labels), margin = 2)

       Clusters
Strokes  Microglia0  Microglia1  Microglia2  Microglia3  Microglia4  Microglia5
  FALSE 0.089622642 0.710227273 0.991666667 0.747826087 1.000000000 0.959459459
  TRUE  0.910377358 0.289772727 0.008333333 0.252173913 0.000000000 0.040540541
       Clusters
Strokes  Microglia6
  FALSE 0.982758621
  TRUE  0.017241379

DimPlot(object = samples.integrated.filt, group.by =  "Peri_ictus_binary", split.by = "Genotype_Treatment")

ggplot(samples.integrated.filt@meta.data, aes(x=Peri_ictus1, y=Control1))+
  geom_density_2d_filled()+geom_point(col="#FFFFFF55")+theme_classic()+ggtitle("all cells")

ggplot(samples.integrated.filt@meta.data[samples.integrated.filt$Peri_ictus_binary==TRUE,], aes(x=Peri_ictus1, y=Control1))+geom_density_2d_filled()+geom_point(col="#FFFFFF55")+theme_classic()+ggtitle("Peri_ictus only")

proportion tables show percebtages of DAM cells per condition

Distals binary proportions

metadata=samples.integrated.filt@meta.data
restab = table(GT_Treat = metadata$Genotype_Treatment, Distals=metadata$Distal_binary)
restab

               Distals
GT_Treat        FALSE TRUE
  WT_Ctrl         229    7
  WT_Stroke       110   16
  APPPS1_Ctrl     195  108
  APPPS1_Stroke   155   32

prop.table(restab, margin = 1)

               Distals
GT_Treat             FALSE       TRUE
  WT_Ctrl       0.97033898 0.02966102
  WT_Stroke     0.87301587 0.12698413
  APPPS1_Ctrl   0.64356436 0.35643564
  APPPS1_Stroke 0.82887701 0.17112299

chisq.test(restab)

    Pearson's Chi-squared test

data:  restab
X-squared = 97.122, df = 3, p-value < 2.2e-16

ggplot(as.data.frame(restab), 
       aes(fill=Distals, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()

library(chisq.posthoc.test)
chisq.posthoc.test(restab)

      Dimension     Value      FALSE       TRUE
1       WT_Ctrl Residuals  7.4251774 -7.4251774
2       WT_Ctrl  p values  0.0000000  0.0000000
3     WT_Stroke Residuals  1.9887921 -1.9887921
4     WT_Stroke  p values  0.3737930  0.3737930
5   APPPS1_Ctrl Residuals -9.1031912  9.1031912
6   APPPS1_Ctrl  p values  0.0000000  0.0000000
7 APPPS1_Stroke Residuals  0.7945767 -0.7945767
8 APPPS1_Stroke  p values  1.0000000  1.0000000

#To Do Pairwise 

a) Distals in APPPS1 vs. DAMs in APPPS1 + Stroke – Venn diagram, or similar

metadata=samples.integrated.filt@meta.data[samples.integrated.filt$Genotype == "APPPS1", ]
restab = table(GT_Treat = as.character(metadata$Genotype_Treatment), Distals=metadata$Distal_binary)
restab

               Distals
GT_Treat        FALSE TRUE
  APPPS1_Ctrl     195  108
  APPPS1_Stroke   155   32

prop.table(restab, margin = 1)

               Distals
GT_Treat            FALSE      TRUE
  APPPS1_Ctrl   0.6435644 0.3564356
  APPPS1_Stroke 0.8288770 0.1711230

fisher.test(restab)

    Fisher's Exact Test for Count Data

data:  restab
p-value = 8.579e-06
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.2304338 0.5934206
sample estimates:
odds ratio 
 0.3734791 

ggplot(as.data.frame(restab), 
       aes(fill=Distals, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()

b) Distals in WT+Stroke vs. DAMs in APPPS1 + Stroke – Venn diagram, or similar

metadata=samples.integrated.filt@meta.data[samples.integrated.filt$Genotype == "WT", ]
restab = table(GT_Treat = as.character(metadata$Genotype_Treatment), Distals=metadata$Distal_binary)
restab

           Distals
GT_Treat    FALSE TRUE
  WT_Ctrl     229    7
  WT_Stroke   110   16

prop.table(restab, margin = 1)

           Distals
GT_Treat         FALSE       TRUE
  WT_Ctrl   0.97033898 0.02966102
  WT_Stroke 0.87301587 0.12698413

fisher.test(restab)

    Fisher's Exact Test for Count Data

data:  restab
p-value = 0.0005206
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
  1.780926 14.027864
sample estimates:
odds ratio 
  4.736223 

ggplot(as.data.frame(restab), 
       aes(fill=Distals, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()

Controls binary proportions

metadata=samples.integrated.filt@meta.data
restab = table(GT_Treat = metadata$Genotype_Treatment, Controls=metadata$Control_binary)
restab

               Controls
GT_Treat        FALSE TRUE
  WT_Ctrl         142   94
  WT_Stroke        90   36
  APPPS1_Ctrl     223   80
  APPPS1_Stroke   142   45

prop.table(restab, margin = 1)

               Controls
GT_Treat            FALSE      TRUE
  WT_Ctrl       0.6016949 0.3983051
  WT_Stroke     0.7142857 0.2857143
  APPPS1_Ctrl   0.7359736 0.2640264
  APPPS1_Stroke 0.7593583 0.2406417

chisq.test(restab)

    Pearson's Chi-squared test

data:  restab
X-squared = 16.007, df = 3, p-value = 0.00113

ggplot(as.data.frame(restab), 
       aes(fill=Controls, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()

library(chisq.posthoc.test)
chisq.posthoc.test(restab)

      Dimension     Value      FALSE       TRUE
1       WT_Ctrl Residuals -3.9061041  3.9061041
2       WT_Ctrl  p values  0.0007500  0.0007500
3     WT_Stroke Residuals  0.3606338 -0.3606338
4     WT_Stroke  p values  1.0000000  1.0000000
5   APPPS1_Ctrl Residuals  1.6700713 -1.6700713
6   APPPS1_Ctrl  p values  0.7592420  0.7592420
7 APPPS1_Stroke Residuals  1.9824864 -1.9824864
8 APPPS1_Stroke  p values  0.3793990  0.3793990

#To Do Pairwise 

a) Controls in APPPS1 vs. DAMs in APPPS1 + Stroke – Venn diagram, or similar

metadata=samples.integrated.filt@meta.data[samples.integrated.filt$Genotype == "APPPS1", ]
restab = table(GT_Treat = as.character(metadata$Genotype_Treatment), Controls=metadata$Control_binary)
restab

               Controls
GT_Treat        FALSE TRUE
  APPPS1_Ctrl     223   80
  APPPS1_Stroke   142   45

prop.table(restab, margin = 1)

               Controls
GT_Treat            FALSE      TRUE
  APPPS1_Ctrl   0.7359736 0.2640264
  APPPS1_Stroke 0.7593583 0.2406417

fisher.test(restab)

    Fisher's Exact Test for Count Data

data:  restab
p-value = 0.5949
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.5645775 1.3721440
sample estimates:
odds ratio 
 0.8835869 

ggplot(as.data.frame(restab), 
       aes(fill=Controls, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()

b) Controls in WT+Stroke vs. DAMs in APPPS1 + Stroke – Venn diagram, or similar

metadata=samples.integrated.filt@meta.data[samples.integrated.filt$Genotype == "WT", ]
restab = table(GT_Treat = as.character(metadata$Genotype_Treatment), Controls=metadata$Control_binary)
restab

           Controls
GT_Treat    FALSE TRUE
  WT_Ctrl     142   94
  WT_Stroke    90   36

prop.table(restab, margin = 1)

           Controls
GT_Treat        FALSE      TRUE
  WT_Ctrl   0.6016949 0.3983051
  WT_Stroke 0.7142857 0.2857143

fisher.test(restab)

    Fisher's Exact Test for Count Data

data:  restab
p-value = 0.03847
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.3671052 0.9850947
sample estimates:
odds ratio 
 0.6050892 

ggplot(as.data.frame(restab), 
       aes(fill=Controls, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()

Strokes binary proportions

metadata=samples.integrated.filt@meta.data
restab = table(GT_Treat = metadata$Genotype_Treatment, Peri_Ictal=metadata$Peri_ictus_binary)
restab

               Peri_Ictal
GT_Treat        FALSE TRUE
  WT_Ctrl         235    1
  WT_Stroke        59   67
  APPPS1_Ctrl     151  152
  APPPS1_Stroke   129   58

prop.table(restab, margin = 1)

               Peri_Ictal
GT_Treat              FALSE        TRUE
  WT_Ctrl       0.995762712 0.004237288
  WT_Stroke     0.468253968 0.531746032
  APPPS1_Ctrl   0.498349835 0.501650165
  APPPS1_Stroke 0.689839572 0.310160428

chisq.test(restab)

    Pearson's Chi-squared test

data:  restab
X-squared = 178.15, df = 3, p-value < 2.2e-16

ggplot(as.data.frame(restab), 
       aes(fill=Peri_Ictal, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()

library(chisq.posthoc.test)
chisq.posthoc.test(restab)

      Dimension     Value      FALSE        TRUE
1       WT_Ctrl Residuals 12.4100951 -12.4100951
2       WT_Ctrl  p values  0.0000000   0.0000000
3     WT_Stroke Residuals -5.3286184   5.3286184
4     WT_Stroke  p values  0.0000010   0.0000010
5   APPPS1_Ctrl Residuals -8.1104408   8.1104408
6   APPPS1_Ctrl  p values  0.0000000   0.0000000
7 APPPS1_Stroke Residuals  0.5325286  -0.5325286
8 APPPS1_Stroke  p values  1.0000000   1.0000000

#To Do Pairwise 

a) Strokes in APPPS1 vs. DAMs in APPPS1 + Stroke – Venn diagram, or similar

metadata=samples.integrated.filt@meta.data[samples.integrated.filt$Genotype == "APPPS1", ]
restab = table(GT_Treat = as.character(metadata$Genotype_Treatment), Peri_Ictal=metadata$Peri_ictus_binary)
restab

               Peri_Ictal
GT_Treat        FALSE TRUE
  APPPS1_Ctrl     151  152
  APPPS1_Stroke   129   58

prop.table(restab, margin = 1)

               Peri_Ictal
GT_Treat            FALSE      TRUE
  APPPS1_Ctrl   0.4983498 0.5016502
  APPPS1_Stroke 0.6898396 0.3101604

fisher.test(restab)

    Fisher's Exact Test for Count Data

data:  restab
p-value = 3.416e-05
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.2983945 0.6660541
sample estimates:
odds ratio 
 0.4473919 

ggplot(as.data.frame(restab), 
       aes(fill=Peri_Ictal, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()

b) Strokes in WT+Stroke vs. DAMs in APPPS1 + Stroke – Venn diagram, or similar

metadata=samples.integrated.filt@meta.data[samples.integrated.filt$Genotype == "WT", ]
restab = table(GT_Treat = as.character(metadata$Genotype_Treatment), Peri_Ictal=metadata$Peri_ictus_binary)
restab

           Peri_Ictal
GT_Treat    FALSE TRUE
  WT_Ctrl     235    1
  WT_Stroke    59   67

prop.table(restab, margin = 1)

           Peri_Ictal
GT_Treat          FALSE        TRUE
  WT_Ctrl   0.995762712 0.004237288
  WT_Stroke 0.468253968 0.531746032

fisher.test(restab)

    Fisher's Exact Test for Count Data

data:  restab
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
   43.61937 9976.27448
sample estimates:
odds ratio 
  261.1922 

ggplot(as.data.frame(restab), 
       aes(fill=Peri_Ictal, x=GT_Treat, y=Freq))+
  geom_bar(position="fill", stat="identity")+theme_classic()

Alternative appraoch to identify cell types (assuming the cell types are mutaully exclusive)

library(scSorter)

annodf = data.frame(Type = "Periictus", Marker = Stroke_genes)
annodf = rbind(annodf, data.frame(Type = "Control", Marker = control_genes))
annodf = rbind(annodf, data.frame(Type = "Distal", Marker = distal_genes))


spatialtypes = scSorter(samples.integrated.filt@assays$SCT@counts, annodf) #makes the calling 

Warning in data_preprocess(expr, anno_processed): The following specified
marker genes are not found from the expression data: SERPINA3N, TMSB4X, TIMP1,
ATP5MD, NRGN, NORAD, DNM1, GM15564, CX3CL1, GM14539, GNG13, STMN3, PRXL2B,
H2AZ1, CTXN1, NRSN1, BEX1, CCK.

metadata = samples.integrated.filt@meta.data 
metadata = metadata[,! colnames(metadata) %in% c("PredType")]

samples.integrated.filt@meta.data <- cbind(metadata, PredType = spatialtypes$Pred_Type)

DimPlot(samples.integrated.filt, group.by = "PredType", split.by = "Genotype_Treatment")

samples.integrated.filt.comorbid <- samples.integrated.filt[,metadata$Genotype_Treatment == "APPPS1_Stroke"]
DimPlot(samples.integrated.filt.comorbid, group.by = "PredType", )

crosstab = table(Condition = samples.integrated.filt$Genotype_Treatment, Spatial_Type = samples.integrated.filt$PredType)


ggplot(as.data.frame(crosstab), aes(fill=Spatial_Type, x=Condition, y=Freq)) + geom_bar(position="fill", stat="identity")+theme_classic()

crosstab

               Spatial_Type
Condition       Control Distal Periictus Unknown
  WT_Ctrl            12      0       221       3
  WT_Stroke          13      3       108       2
  APPPS1_Ctrl        10      0       292       1
  APPPS1_Stroke      46     14       113      14

chisq.test(crosstab)

Warning in chisq.test(crosstab): Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  crosstab
X-squared = 148.47, df = 9, p-value < 2.2e-16

ggplot(as.data.frame(crosstab[1:2,]), aes(fill=Spatial_Type, x=Condition, y=Freq)) + geom_bar(position="fill", stat="identity")+theme_classic()

crosstab[1:2,]

           Spatial_Type
Condition   Control Distal Periictus Unknown
  WT_Ctrl        12      0       221       3
  WT_Stroke      13      3       108       2

chisq.test(crosstab[1:2,])

Warning in chisq.test(crosstab[1:2, ]): Chi-squared approximation may be
incorrect

    Pearson's Chi-squared test

data:  crosstab[1:2, ]
X-squared = 9.5037, df = 3, p-value = 0.02329

ggplot(as.data.frame(crosstab[3:4,]), aes(fill=Spatial_Type, x=Condition, y=Freq)) + geom_bar(position="fill", stat="identity")+theme_classic()

crosstab[3:4,]

               Spatial_Type
Condition       Control Distal Periictus Unknown
  APPPS1_Ctrl        10      0       292       1
  APPPS1_Stroke      46     14       113      14

chisq.test(crosstab[3:4,])

    Pearson's Chi-squared test

data:  crosstab[3:4, ]
X-squared = 106, df = 3, p-value < 2.2e-16

DEG cpmparing conditions overall

WT Stroke vs no Stroke

avg_logFC: log fold-chage of the average expression between the two groups. Positive values indicate that the gene is more highly expressed in the first group

Idents(samples.integrated.filt) <- "Genotype_Treatment"
DimPlot(samples.integrated.filt)

DEG_Wt_CtrlvsStroke <-FindMarkers(samples.integrated.filt, 
                                  ident.1 = "WT_Stroke", ident.2 = "WT_Ctrl", 
                                  test.use = "LR", logfc.threshold = 0)

Warning: The following arguments are not used: norm.method

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p = EnhancedVolcano::EnhancedVolcano(DEG_Wt_CtrlvsStroke, x = "avg_log2FC", 
                                 y = "p_val_adj", lab=rownames(DEG_Wt_CtrlvsStroke), title = "WT_Ctrl vs WT_Stroke")

display_tab(DEG_Wt_CtrlvsStroke)

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

p

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Volcano_WTCtrlvsWTStroke.svg", p)

Saving 6 x 6 in image

APPPS1 Stroke vs APPPS1 no Stroke

avg_logFC: log fold-chage of the average expression between the two groups. Positive values indicate that the gene is more highly expressed in the first group

DEG_APPPS1_CtrlvsStroke <-FindMarkers(samples.integrated.filt, 
                                     ident.1 = "APPPS1_Stroke", ident.2 = "APPPS1_Ctrl", 
                                     test.use = "LR", logfc.threshold = 0)

Warning: The following arguments are not used: norm.method

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display_tab(DEG_APPPS1_CtrlvsStroke)

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

p<- EnhancedVolcano::EnhancedVolcano(DEG_APPPS1_CtrlvsStroke, x = "avg_log2FC", y = "p_val_adj", 
                                 lab=rownames(DEG_APPPS1_CtrlvsStroke), title = "APPPS1_Ctrl vs APPPS1_Stroke")


p

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Volcano_APPPS1CtrlvsAPPPS1Stroke.svg", p)

Saving 6 x 6 in image

Scatterplot of DEGs of all four conditions, see https://www.nature.com/articles/s41593-023-01355-y ; see Fig 1e

DEG_Stroke_APPPS1vsWT <-FindMarkers(samples.integrated.filt, 
                                     ident.1 = "APPPS1_Stroke", ident.2 = "WT_Stroke", 
                                     test.use = "LR", logfc.threshold = 0)

Warning: The following arguments are not used: norm.method

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display_tab(DEG_Stroke_APPPS1vsWT)

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

p<- EnhancedVolcano::EnhancedVolcano(DEG_Stroke_APPPS1vsWT, x = "avg_log2FC", y = "p_val_adj", 
                                 lab=rownames(DEG_Stroke_APPPS1vsWT), title = "WT_stroke vs APPPS1_Stroke")


p

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Volcano_WTstrokevsAPPPS1Stroke.svg", p)

Saving 6 x 6 in image

WT ctr; vs APPPS1 ctrl

avg_logFC: log fold-chage of the average expression between the two groups. Positive values indicate that the gene is more highly expressed in the first group

DEG_APPPS1_CtrlvsStroke <-FindMarkers(samples.integrated.filt, 
                                     ident.1 = "APPPS1_Ctrl", ident.2 = "WT_Ctrl", 
                                     test.use = "LR", logfc.threshold = 0)

Warning: The following arguments are not used: norm.method

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display_tab(DEG_APPPS1_CtrlvsStroke)

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

p<- EnhancedVolcano::EnhancedVolcano(DEG_APPPS1_CtrlvsStroke, x = "avg_log2FC", y = "p_val_adj", 
                                 lab=rownames(DEG_APPPS1_CtrlvsStroke), title = "WT_Ctrl vs APPPS1_Ctrl")


p

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Volcano_WTCtrlvsAPPPS1ctrl.svg", p)

Saving 6 x 6 in image

genelist_both= intersect(rownames(DEG_Wt_CtrlvsStroke), rownames(DEG_APPPS1_CtrlvsStroke))

plotdata=data.frame(log2FC_WTCtrl_vs_WTStroke=DEG_Wt_CtrlvsStroke[genelist_both,]$avg_log2FC, log2FC_APPPS1Ctrl_vs_APPPS1Stroke = DEG_APPPS1_CtrlvsStroke[genelist_both,]$avg_log2FC)
rownames(plotdata) = genelist_both

plotdata$label = rownames(plotdata)

n=20
plotdata$label[! c(1:nrow(plotdata)) %in% 
                 c(
                   order(abs(plotdata$log2FC_WTCtrl_vs_WTStroke), decreasing = T)[1:n], 
                   order(abs(plotdata$log2FC_APPPS1Ctrl_vs_APPPS1Stroke), decreasing = T)[1:n])]=""

# plotdata$col="WT"
# apps1=abs(plotdata$APPPS1)>abs(plotdata$WT)
# plotdata$col[ apps1]="APPPS1"
# plotdata$col[(abs(plotdata$APPPS1)<0.25) & (abs(plotdata$WT)<0.25)] <- NA

p <- ggplot(plotdata, aes(x=log2FC_WTCtrl_vs_WTStroke, y=log2FC_APPPS1Ctrl_vs_APPPS1Stroke, label=label))+geom_point(alpha=0.7)+
  geom_text(vjust=-0.7, color="black")+
  #geom_abline(slope=-1, intercept=0, lty=2)+geom_abline(slope=1, intercept=0, lty=2)+
  theme_classic()+ggtitle("log_FC")

p

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_logFCCtrlvsStroke_APPPS1vsWT_all.svg", p)

Saving 7 x 5 in image

# sig only

genelist_both= unique(c(rownames(DEG_Wt_CtrlvsStroke[DEG_Wt_CtrlvsStroke$p_val<0.05,]), rownames(DEG_APPPS1_CtrlvsStroke[DEG_APPPS1_CtrlvsStroke$p_val>0.05,])))

plotdata=data.frame(log2FC_WTCtrl_vs_WTStroke=DEG_Wt_CtrlvsStroke[genelist_both,]$avg_log2FC, log2FC_APPPS1Ctrl_vs_APPPS1Stroke = DEG_APPPS1_CtrlvsStroke[genelist_both,]$avg_log2FC)
rownames(plotdata) = genelist_both

plotdata$label = rownames(plotdata)

n=15
plotdata$label[! c(1:nrow(plotdata)) %in% 
                 c(
                   order(abs(plotdata$log2FC_WTCtrl_vs_WTStroke), decreasing = T)[1:n], 
                   order(abs(plotdata$log2FC_APPPS1Ctrl_vs_APPPS1Stroke), decreasing = T)[1:n])]=""

# plotdata$col="WT"
# apps1=abs(plotdata$APPPS1)>abs(plotdata$WT)
# plotdata$col[ apps1]="APPPS1"
# plotdata$col[(abs(plotdata$APPPS1)<0.25) & (abs(plotdata$WT)<0.25)] <- NA

p <- ggplot(plotdata, aes(x=log2FC_WTCtrl_vs_WTStroke, y=log2FC_APPPS1Ctrl_vs_APPPS1Stroke, label=label))+geom_point(alpha=0.7)+
  geom_text(vjust=-0.7, color="black")+
  #geom_abline(slope=-1, intercept=0, lty=2)+geom_abline(slope=1, intercept=0, lty=2)+
  theme_classic()+ggtitle("log_FC")

p

Warning: Removed 301 rows containing missing values (`geom_point()`).

Warning: Removed 301 rows containing missing values (`geom_text()`).

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_logFCCtrlvsStroke_APPPS1vsWT_sig_only.svg", p)

Saving 7 x 5 in image

Warning: Removed 301 rows containing missing values (`geom_point()`).
Removed 301 rows containing missing values (`geom_text()`).

reads_data = samples.integrated.filt@assays$RNA@scale.data

barplot_genes = function(gene, split.by.cluster =T){
  library(ggbeeswarm)
  plotdata = data.frame(log2Reads = log2(reads_data[gene,]+1), 
                        Genotype_Treatment = samples.integrated.filt@meta.data$Genotype_Treatment, 
                        Cluster = samples.integrated.filt@meta.data$labels)
  p=ggplot(plotdata, aes(x=Genotype_Treatment, y=log2Reads, fill=Genotype_Treatment))+geom_boxplot()+geom_beeswarm(alpha=0.4)+ggtitle(gene)+theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
  if(split.by.cluster){p=p+facet_wrap(~Cluster)}
  p
}

barplot_genes(gene="Spp1", split.by.cluster =T)

DEG_Strokebins_Wt_CtrlvsStroke <-FindMarkers(samples.integrated.filt, subset.ident = "Stroke_bin", subset="TRUE", 
                                  ident.1 = "WT_Stroke", ident.2 = "WT_Ctrl", 
                                  test.use = "LR", logfc.threshold = 0)

Warning: The following arguments are not used: norm.method, subset

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display_tab(DEG_Strokebins_Wt_CtrlvsStroke)

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

DEG_Strokebins_Appps1_CtrlvsStroke <-FindMarkers(samples.integrated.filt, subset.ident = "Stroke_bin", subset="TRUE", 
                                  ident.1 = "APPPS1_Stroke", ident.2 = "APPPS1_Ctrl", 
                                  test.use = "LR", logfc.threshold = 0)

Warning: The following arguments are not used: norm.method, subset

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display_tab(DEG_Strokebins_Appps1_CtrlvsStroke)

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

genelist_both= intersect(rownames(DEG_Strokebins_Appps1_CtrlvsStroke), rownames(DEG_Strokebins_Wt_CtrlvsStroke))

plotdata=data.frame(WT=DEG_Strokebins_Wt_CtrlvsStroke[genelist_both,]$avg_log2FC, APPPS1 = DEG_Strokebins_Appps1_CtrlvsStroke[genelist_both,]$avg_log2FC)
rownames(plotdata) = genelist_both

plotdata$label = rownames(plotdata)
n=5
plotdata$label[! c(1:nrow(plotdata)) %in% 
                 c(
                   order(abs(plotdata$WT), decreasing = T)[1:n], 
                   order(abs(plotdata$APPPS1), decreasing = T)[1:n])]=""

plotdata$col="WT"
apps1=abs(plotdata$APPPS1)>abs(plotdata$WT)
plotdata$col[ apps1]="APPPS1"
plotdata$col[(abs(plotdata$APPPS1)<0.25) & (abs(plotdata$WT)<0.25)] <- NA
ggplot(plotdata, aes(x=WT, y=APPPS1, color=col, label=label))+geom_point(alpha=0.7)+
  geom_text(vjust=-0.7, color="black")+geom_abline(slope=-1, intercept=0, lty=2)+geom_abline(slope=1, intercept=0, lty=2)+theme_classic()+ggtitle("log_FC_Stroke_bins")

DAMS in APPS1 Wt vs Stroke

to do selection of DAMS only compare APPPS1CTRL vs APPS1 Stroke to do selection of DAMs only compare APPPS1Stroke vs WT_Stroke

Dot plot showing top markers for condition clusters, same paper Fig 2f

reads = samples.integrated.filt@assays$SCT@counts

reads.filt= reads[cluster.genes,]

datatoplot_mean = aggregate(t(reads.filt), 
                            list(paste0(samples.integrated.filt$labels, "/",samples.integrated.filt$Genotype_Treatment)), 
                            mean, na.rm=T) %>% 
  as.data.frame() %>% 
  column_to_rownames("Group.1") %>% scale()



datatoplot_mean=melt(datatoplot_mean)

Warning in melt(datatoplot_mean): The melt generic in data.table has been
passed a matrix and will attempt to redirect to the relevant reshape2 method;
please note that reshape2 is deprecated, and this redirection is now deprecated
as well. To continue using melt methods from reshape2 while both libraries are
attached, e.g. melt.list, you can prepend the namespace like
reshape2::melt(datatoplot_mean). In the next version, this warning will become
an error.

datatoplot_mean$Microglia = factor(gsub("/.*", "", datatoplot_mean$Var1), levels=rev(levels(samples.integrated.filt$labels)))
datatoplot_mean$Genotype_Treatment = factor(gsub(".*/", "", datatoplot_mean$Var1), levels=c("WT_Ctrl", "WT_Stroke", "APPPS1_Ctrl","APPPS1_Stroke")  )                                
                                   
colnames(datatoplot_mean) = c("Cluster_Condition", "Gene", "Scaled_mean", "Microglia", "Genotype_Treatment")

datatoplot_perc = aggregate(t(reads.filt), list(paste0(samples.integrated.filt$labels, "/",samples.integrated.filt$Genotype_Treatment)), 
                            function(x){length(which(x>0))/length(x)})

datatoplot_perc=melt(datatoplot_perc, value.name = "perc")

Warning in melt(datatoplot_perc, value.name = "perc"): The melt generic in
data.table has been passed a data.frame and will attempt to redirect to the
relevant reshape2 method; please note that reshape2 is deprecated, and this
redirection is now deprecated as well. To continue using melt methods from
reshape2 while both libraries are attached, e.g. melt.list, you can prepend the
namespace like reshape2::melt(datatoplot_perc). In the next version, this
warning will become an error.

Using Group.1 as id variables

datatoplot = cbind(datatoplot_mean, perc=datatoplot_perc$perc)

p <- ggplot(datatoplot, aes(x=Gene, y=Microglia, col=Scaled_mean, size=perc))+geom_point()+scale_size_continuous(range = c(0,3))+
    theme_classic()+theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+facet_grid(~Genotype_Treatment)

p

ggsave(filename = "/files/scSeq_Hefendehl/output/Microglia_Dotplot_ClsuterCondition_Genelists.svg", p)

Saving 18 x 5 in image

Pseudotime analysis

samples.integrated.filt.meta = samples.integrated.filt@meta.data
sce.microglia=as.SingleCellExperiment(samples.integrated.filt)
sce.microglia <- slingshot(sce.microglia, clusterLabels = 'labels', reducedDim = 'UMAP', start.clus="Microglia2")

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

samples.integrated.filt.meta$Pseudotime_1 <- sce.microglia$slingPseudotime_1
samples.integrated.filt.meta$Pseudotime_2 <- sce.microglia$slingPseudotime_2
samples.integrated.filt.meta$Pseudotime_3 <- sce.microglia$slingPseudotime_3

samples.integrated.filt.meta -> samples.integrated.filt@meta.data

dataset = data.frame(reducedDim(sce.microglia, "UMAP"), 
                     cluster = as.character(sce.microglia$labels), 
                     DAMs = as.character(sce.microglia$DAM_binary))

curves <- slingCurves(sce.microglia, as.df=T)

p = ggplot(dataset, aes(x=umap_1, y=umap_2))+
  geom_point(aes(col = cluster), size=2)+
  geom_point(data=subset(dataset, dataset$DAMs==T), aes(fill=DAMs), pch=21, size=0.7)+
  scale_fill_manual(values="black")
curves = curves %>% arrange(Lineage, Order)

p = p + geom_path(data = curves, aes(group = Lineage), lwd=1, lineend = "butt",)+theme_classic()

curves_arrows = curves %>% group_by(Lineage) %>%  mutate(xend = lead(umap_1), yend = lead(umap_2))

curves_arrows = curves_arrows[curves_arrows$Order %in% seq(10,150, by=floor((150-10)/10)),]

p = p + geom_segment(data = curves_arrows,
                     aes(x = umap_1, y = umap_2, xend = xend, yend = yend),
                     linewidth=0.5, 
                     arrow = arrow(length=unit(0.3, "cm"), type="closed"))
p

Warning: Removed 3 rows containing missing values (`geom_segment()`).

ggsave(filename = "/files/scSeq_Hefendehl/docs/Pseudotime_linages.svg", p)

Saving 7 x 5 in image

Warning: Removed 3 rows containing missing values (`geom_segment()`).

ggsave(filename = "/files/scSeq_Hefendehl/docs/Pseudotime_linages.pdf", p)

Saving 7 x 5 in image

Warning: Removed 3 rows containing missing values (`geom_segment()`).

library(TSCAN)

n.top.gene = 10

sce.microglia.PT1<- testPseudotime(sce.microglia, pseudotime = sce.microglia$slingPseudotime_1)
sce.microglia.PT2<- testPseudotime(sce.microglia, pseudotime = sce.microglia$slingPseudotime_2)
sce.microglia.PT3<- testPseudotime(sce.microglia, pseudotime = sce.microglia$slingPseudotime_3)

Look for genes that are up/down-regulated along pseudotime, for the re-clustered cells (Same paper Fig 2G/H)

Trajectory 1 genes

By default, estimates of the spline coefficients are not returned as they are difficult to interpret. Rather, a log-fold change of expression along each path is estimated to provide some indication of the overall magnitude and direction of any change.

sce.microglia.PT1 <- sce.microglia.PT1[order(sce.microglia.PT1$FDR, decreasing = F),]
display_tab(as.data.frame(sce.microglia.PT1))

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

PT1_up = sce.microglia.PT1[sce.microglia.PT1$logFC>0,]
PT1_low = sce.microglia.PT1[sce.microglia.PT1$logFC<0,]

p <- FeaturePlot(samples.integrated.filt, features = "Pseudotime_1")+scale_color_viridis()

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

p

ggsave(filename = "/files/scSeq_Hefendehl/docs/Pseudotime1.svg", p)

Saving 7 x 5 in image

a=scater::plotExpression(sce.microglia, features=rownames(PT1_up)[1:n.top.gene], x="slingPseudotime_1", colour_by="labels")+scale_color_hue()

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

b=scater::plotExpression(sce.microglia, features=rownames(PT1_low)[1:n.top.gene], x="slingPseudotime_1", colour_by="labels")+scale_color_hue()

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

a|b

Warning: Removed 2250 rows containing missing values (`geom_point()`).
Removed 2250 rows containing missing values (`geom_point()`).

ggsave(filename = "/files/scSeq_Hefendehl/docs/Pseudotime1_Top10up_down_reg_Genes-correct.svg", p)

Saving 8 x 5 in image

Trajectory 2 genes

sce.microglia.PT2 <- sce.microglia.PT2[order(sce.microglia.PT2$FDR, decreasing = F),]
display_tab(as.data.frame(sce.microglia.PT2))

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

PT2_up = sce.microglia.PT2[sce.microglia.PT2$logFC>0,]
PT2_low = sce.microglia.PT2[sce.microglia.PT2$logFC<0,]
p <- FeaturePlot(samples.integrated.filt, features = "Pseudotime_2")+scale_color_viridis()

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

p

ggsave(filename = "/files/scSeq_Hefendehl/docs/Pseudotime2.svg", p)

Saving 7 x 5 in image

a<-scater::plotExpression(sce.microglia, features=rownames(PT2_up)[1:n.top.gene], x="slingPseudotime_2", colour_by="labels")+scale_color_hue()

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

b<-scater::plotExpression(sce.microglia, features=rownames(PT2_low)[1:n.top.gene], x="slingPseudotime_2", colour_by="labels")+scale_color_hue()

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

a|b

Warning: Removed 2770 rows containing missing values (`geom_point()`).
Removed 2770 rows containing missing values (`geom_point()`).

ggsave(filename = "/files/scSeq_Hefendehl/docs/Pseudotime2_Top10up_down_reg_Genes.svg", a|b)

Saving 8 x 5 in image

Warning: Removed 2770 rows containing missing values (`geom_point()`).
Removed 2770 rows containing missing values (`geom_point()`).

Trajectory 3 genes

sce.microglia.PT3 <- sce.microglia.PT3[order(sce.microglia.PT3$FDR, decreasing = F),]
display_tab(as.data.frame(sce.microglia.PT3))

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

PT3_up = sce.microglia.PT3[sce.microglia.PT3$logFC>0,]
PT3_low = sce.microglia.PT3[sce.microglia.PT3$logFC<0,]

p <- FeaturePlot(samples.integrated.filt, features = "Pseudotime_3")+scale_color_viridis()

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

p

ggsave(filename = "/files/scSeq_Hefendehl/docs/Pseudotime3.svg", p)

Saving 7 x 5 in image

a<-scater::plotExpression(sce.microglia, features=rownames(PT3_up)[1:n.top.gene], x="slingPseudotime_3", colour_by="labels")+scale_color_hue()

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

b<-scater::plotExpression(sce.microglia, features=rownames(PT3_low)[1:n.top.gene], x="slingPseudotime_3", colour_by="labels")+scale_color_hue()

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

a|b

Warning: Removed 3420 rows containing missing values (`geom_point()`).
Removed 3420 rows containing missing values (`geom_point()`).

ggsave(filename = "/files/scSeq_Hefendehl/docs/Pseudotime3_Top10up_down_reg_Genes.svg", a|b)

Saving 8 x 5 in image

Warning: Removed 3420 rows containing missing values (`geom_point()`).
Removed 3420 rows containing missing values (`geom_point()`).

Marker for DAM onyl DEG for APPPS1_Ctrl vs APPPS_1 Stroke; WT_Stroke vs APPPS1_Stroke

WGCNA

In the paper https://www.nature.com/articles/s41586-018-0023-4 this analysis was used to generate different modules of gene lists that can then be mapped onto different treatment/genotypes within one experiment (see Figure 4). This is an analysis that can be done in Seurat https://smorabit.github.io/tutorials/9_scWGCNA_tutorial/ Which could be very cool for our data. In the paper they also have 3 different conditions in 2 genotypes.

avoid running this section as it takes very long

nclusters <- length(unique(samples.integrated.filt$labels))
genes.use <- rownames(samples.integrated.filt)
targets <- samples.integrated.filt@meta.data

group <- as.factor(samples.integrated.filt$Genotype_Treatment)

force.recalc = T
powers = c(seq(1,10,by=1), seq(12,20, by=2));

if(force.recalc | (! file.exists(file="/files/scSeq_Hefendehl/output/net.rds"))){ # skips recalculation of net object as it takes long... 
  
  datExpr <- as.data.frame(GetAssayData(samples.integrated.filt, assay='RNA', slot='data')[genes.use,])
  datExpr <- as.data.frame(t(datExpr))
  datExpr <- datExpr[,goodGenes(datExpr)]
  
  # expressed in more than 10% of cells 
  exprgenes = colSums(datExpr>0)>nrow(datExpr)*0.1
  datExpr <- datExpr[,exprgenes]
  
  #annotated only 
  
  
  mmacc=as.list(org.Mm.egALIAS2EG)
  annotatedgenes = colnames(datExpr) %in% names(mmacc)
  datExpr <- datExpr[,annotatedgenes]
  
  powers = c(seq(1,10,by=1), seq(12,20, by=2));
  
  
  enableWGCNAThreads()
  
  # Call the network topology analysis function for each set in turn
  powerTable = list(
    data = pickSoftThreshold(
      datExpr,
      powerVector=powers,
      verbose = 100,
      networkType="unsigned",
      corFnc="cor"
    )[[2]]
  );
  
  
  colors = c("blue", "red","black")
  # Will plot these columns of the returned scale free analysis tables
  plotCols = c(2,5,6,7)
  colNames = c("Scale Free Topology Model Fit", "Mean connectivity", "mean connectivity",
               "Max connectivity");
  
  # Get the minima and maxima of the plotted points
  ylim = matrix(NA, nrow = 2, ncol = 4);
  for (col in 1:length(plotCols)){
    ylim[1, col] = min(ylim[1, col], powerTable$data[, plotCols[col]], na.rm = TRUE);
    ylim[2, col] = max(ylim[2, col], powerTable$data[, plotCols[col]], na.rm = TRUE);
  }
  
  # Plot the quantities in the chosen columns vs. the soft thresholding power
  
  par(mfcol = c(2,2));
  par(mar = c(4.2, 4.2 , 2.2, 0.5))
  cex1 = 0.7;
  
  for (col in 1:length(plotCols)){
    plot(powerTable$data[,1], -sign(powerTable$data[,3])*powerTable$data[,2],
         xlab="Soft Threshold (power)",ylab=colNames[col],type="n", ylim = ylim[, col],
         main = colNames[col]);
    addGrid();
    
    if (col==1){
      text(powerTable$data[,1], -sign(powerTable$data[,3])*powerTable$data[,2],
           labels=powers,cex=cex1,col=colors[1]);
    } else
      text(powerTable$data[,1], powerTable$data[,plotCols[col]],
           labels=powers,cex=cex1,col=colors[1]);
    if (col==1){
      legend("bottomright", legend = 'Metacells', col = colors, pch = 20) ;
    } else
      legend("topright", legend = 'Metacells', col = colors, pch = 20) ;
  }

  softPower=3
  
  nSets = 1
  setLabels = 'OCS'
  shortLabels = setLabels
  
  multiExpr <- list()
  multiExpr[['ODC']] <- list(data=datExpr)
  
  checkSets(multiExpr) # check data size
  
  # construct network

  net=blockwiseConsensusModules(multiExpr, blocks = NULL,
                                maxBlockSize = 30000, ## This should be set to a smaller size if the user has limited RAM
                                randomSeed = 12345,
                                corType = "pearson",
                                power = softPower,
                                consensusQuantile = 0.3,
                                networkType = "unsigned",
                                TOMType = "signed",
                                TOMDenom = "min",
                                scaleTOMs = TRUE, scaleQuantile = 0.8,
                                sampleForScaling = TRUE, sampleForScalingFactor = 1000,
                                useDiskCache = TRUE, chunkSize = NULL,
                                deepSplit = 4,
                                pamStage=FALSE,
                                detectCutHeight = 0.995, minModuleSize = 50,
                                mergeCutHeight = 0.2,
                                saveConsensusTOMs = TRUE,
                                consensusTOMFilePattern = "ConsensusTOM-block.%b.rda")
  
  saveRDS(net, file="/files/scSeq_Hefendehl/output/net.rds")
  saveRDS(powerTable, file="/files/scSeq_Hefendehl/output/powertable.rds")
} else {
  net = readRDS(file="/files/scSeq_Hefendehl/output/net.rds")
  powerTable = readRDS(file="/files/scSeq_Hefendehl/output/powertable.rds")
  powers = c(seq(1,10,by=1), seq(12,20, by=2));
  colors = c("blue", "red","black")
  # Will plot these columns of the returned scale free analysis tables
  plotCols = c(2,5,6,7)
  colNames = c("Scale Free Topology Model Fit", "Mean connectivity", "mean connectivity",
               "Max connectivity");
  
  # Get the minima and maxima of the plotted points
  ylim = matrix(NA, nrow = 2, ncol = 4);
  for (col in 1:length(plotCols)){
    ylim[1, col] = min(ylim[1, col], powerTable$data[, plotCols[col]], na.rm = TRUE);
    ylim[2, col] = max(ylim[2, col], powerTable$data[, plotCols[col]], na.rm = TRUE);
  }
  
  # Plot the quantities in the chosen columns vs. the soft thresholding power
  
  par(mfcol = c(2,2));
  par(mar = c(4.2, 4.2 , 2.2, 0.5))
  cex1 = 0.7;
  
  for (col in 1:length(plotCols)){
    plot(powerTable$data[,1], -sign(powerTable$data[,3])*powerTable$data[,2],
         xlab="Soft Threshold (power)",ylab=colNames[col],type="n", ylim = ylim[, col],
         main = colNames[col]);
    addGrid();
    
    if (col==1){
      text(powerTable$data[,1], -sign(powerTable$data[,3])*powerTable$data[,2],
           labels=powers,cex=cex1,col=colors[1]);
    } else
      text(powerTable$data[,1], powerTable$data[,plotCols[col]],
           labels=powers,cex=cex1,col=colors[1]);
    if (col==1){
      legend("bottomright", legend = 'Metacells', col = colors, pch = 20) ;
    } else
      legend("topright", legend = 'Metacells', col = colors, pch = 20) ;
  }
}

Warning: The `slot` argument of `GetAssayData()` is deprecated as of SeuratObject 5.0.0.
ℹ Please use the `layer` argument instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.

  ..Excluding 4 genes from the calculation due to too many missing samples or zero variance.
Allowing parallel execution with up to 15 working processes.
pickSoftThreshold: will use block size 5225.
 pickSoftThreshold: calculating connectivity for given powers...
   ..working on genes 1 through 5225 of 8562
   ..working on genes 5226 through 8562 of 8562
   Power SFT.R.sq  slope truncated.R.sq  mean.k. median.k.  max.k.
1      1    0.950 -4.140          0.970 3.49e+02  3.12e+02 1200.00
2      2    0.982 -2.710          0.994 2.73e+01  1.89e+01  265.00
3      3    0.977 -1.960          0.987 3.74e+00  1.54e+00   85.50
4      4    0.992 -1.520          0.990 8.94e-01  1.55e-01   38.10
5      5    0.988 -1.340          0.986 3.34e-01  1.80e-02   24.60
6      6    0.988 -1.250          0.985 1.67e-01  2.31e-03   18.90
7      7    0.986 -1.220          0.982 1.00e-01  3.32e-04   15.10
8      8    0.931 -1.190          0.911 6.70e-02  5.30e-05   12.40
9      9    0.943 -1.140          0.928 4.82e-02  8.90e-06   10.40
10    10    0.981 -1.080          0.975 3.65e-02  1.57e-06    8.83
11    12    0.910 -1.080          0.884 2.32e-02  5.30e-08    6.55
12    14    0.952 -1.010          0.939 1.62e-02  2.05e-09    4.99
13    16    0.954 -0.994          0.952 1.21e-02  8.53e-11    3.87
14    18    0.945 -0.962          0.962 9.50e-03  3.66e-12    3.04
15    20    0.923 -0.989          0.921 7.75e-03  1.62e-13    3.00

 Calculating consensus modules and module eigengenes block-wise from all genes
 Calculating topological overlaps block-wise from all genes
   Flagging genes and samples with too many missing values...
    ..step 1
    TOM calculation: adjacency..
    ..will use 15 parallel threads.
     Fraction of slow calculations: 0.000000
    ..connectivity..
    ..matrix multiplication (system BLAS)..
    ..normalization..
    ..done.
 ..Working on block 1 .
 ..Working on block 1 .
 ..merging consensus modules that are too close..

consMEs = net$multiMEs;
moduleLabels = net$colors;


# Convert the numeric labels to color labels
moduleColors = as.character(moduleLabels)
consTree = net$dendrograms[[1]];



# module eigengenes
MEs=moduleEigengenes(multiExpr[[1]]$data, colors = moduleColors, nPC=1)$eigengenes
MEs=orderMEs(MEs)
meInfo<-data.frame(rownames(datExpr), MEs)
colnames(meInfo)[1]= "SampleID"

# intramodular connectivity
KMEs<-signedKME(datExpr, MEs,outputColumnName = "kME",corFnc = "bicor")

Warning in bicor(datExpr, datME, , use = "p"): bicor: zero MAD in variable 'x'.
Pearson correlation was used for individual columns with zero (or missing) MAD.

# compile into a module metadata table
geneInfo=as.data.frame(cbind(colnames(datExpr),moduleColors, KMEs))

# how many modules did we get?
nmodules <- length(unique(moduleColors))

# merged gene symbol column
colnames(geneInfo)[1]= "GeneSymbol"
colnames(geneInfo)[2]= "Initially.Assigned.Module.Color"

# save info
write.csv(geneInfo,file=paste0('/files/scSeq_Hefendehl/output/geneInfoSigned.csv'))

PCvalues=MEs

p = WGCNA::plotDendroAndColors(consTree, moduleColors[net$goodGenes], "Module colors", dendroLabels = FALSE, hang = 0.03, addGuide = TRUE, guideHang = 0.05,
main = paste0("Microglia  gene dendrogram and module colors"))

ggsave(file="/files/scSeq_Hefendehl/output/Microglia_WGCNA_Dendrocolplot.svg")

Saving 7 x 5 in image

Warning: Removed 3420 rows containing missing values (`geom_point()`).
Removed 3420 rows containing missing values (`geom_point()`).

## Todo adjust 
plot_df <- cbind(dplyr::select(targets, c(Genotype, Treatment)), PCvalues)
plot_df <- reshape2::melt(plot_df, id.vars = c('Genotype', 'Treatment'))
plot_df$Genotype <- factor(plot_df$Genotype, levels=c('WT','APPPS1'))
plot_df$Genotype_Treatment <- paste0(plot_df$Genotype, "_", plot_df$Treatment)
plot_df$Genotype_Treatment <- factor(plot_df$Genotype_Treatment, levels=c('WT_Ctrl','WT_Stroke', "APPPS1_Ctrl", "APPPS1_Stroke"))

colors <- sub('ME', '', as.character(levels(plot_df$variable)))
p <- ggplot(plot_df, aes(x=variable, y=value, fill=Treatment)) +
  geom_boxplot(notch=FALSE) +
  RotatedAxis() + ylab('Module Eigengene') + xlab('') +
  theme(
    axis.text.x=element_blank(),
    axis.ticks.x=element_blank(),
  )

p <- p + facet_wrap(~variable+Genotype, scales='free_x', ncol=nmodules)+theme_classic()

p

p2 <- ggplot(plot_df, aes(x=variable, y=value, fill=Genotype_Treatment)) +
  geom_violin() +
 # RotatedAxis() + 
  ylab('Module Eigengene') + xlab('') +
  theme(
    axis.text.x=element_blank(),
    axis.ticks.x=element_blank(),
  )

p2 <- p2 + facet_wrap(~variable, scales='free', ncol=nmodules)+theme_classic()

p2

ggsave(file="/files/scSeq_Hefendehl/output/Microgila_WGCNA_Eigenscore.svg", p2)

Saving 6 x 2 in image

reslist = list()
for(M in unique(plot_df$variable)){

  Mdata = plot_df[plot_df$variable==M,]
  fit= lm(value~Genotype*Treatment, data = Mdata)
  resfit = summary(fit)
  reslist[[M]] <- c(Genotype_Treatment = resfit$coefficients["GenotypeAPPPS1:TreatmentStroke", "Pr(>|t|)"])
  }

ME_avg <- plot_df %>% dplyr::group_by(variable, Treatment, Genotype) %>% summarise(avg_Eigenmodule = mean(value),
                                                                                   sd_Eigenvalue = sd(value),
                                                                                   med_Eigenvalue=median(value)) 

`summarise()` has grouped output by 'variable', 'Treatment'. You can override
using the `.groups` argument.

ME_avg$pval=unlist(reslist[ME_avg$variable])
ME_avg$pval_ast=convertpvaltostars(ME_avg$pval)
ME_avg$Genotype_Treatment = paste0(ME_avg$Genotype, "_",ME_avg$Treatment)
ME_avg$Genotype_Treatment <- factor(ME_avg$Genotype_Treatment, levels=c('WT_Ctrl','WT_Stroke', "APPPS1_Ctrl", "APPPS1_Stroke"))
ME_avg$Module = paste0(ME_avg$pval_ast, ME_avg$variable)

ggplot(ME_avg, aes(x=Genotype_Treatment, y=Module, fill=avg_Eigenmodule))+geom_tile()+scale_fill_viridis()+theme_classic()+theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

Meandata = MEs %>% group_by(samples.integrated.filt$Genotype_Treatment) %>% summarise_all(mean) %>% column_to_rownames("samples.integrated.filt$Genotype_Treatment") %>% t()
SDdata = MEs %>% group_by(samples.integrated.filt$Genotype_Treatment) %>% summarise_all(sd)%>% column_to_rownames("samples.integrated.filt$Genotype_Treatment") %>% t()
colLabels=colnames(Meandata)
rowLabels=rownames(Meandata)
textlab = paste0(signif(Meandata,2), "\n", "(", signif(SDdata, 2), ")")

svg("/files/scSeq_Hefendehl/output/WGCNA_labeled_Heatmap.svg")
labeledHeatmap(Meandata, xLabels = colLabels, 
               yLabels = rowLabels,
               colorLabels = TRUE,
               colors = viridis(50),
               setStdMargins = FALSE, textMatrix = textlab,
               cex.col="white",
               main = "Mean(SD) Eigengene-expression");

Warning in plot.window(xlim, ylim, log = log, ...): "cex.col" is not a
graphical parameter

Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
"cex.col" is not a graphical parameter

dev.off()

png 
  2 

Modules = unique(moduleColors)
Modules = Modules[! Modules %in% "grey"] # drop grey one

for(m in Modules){
  
p = DoHeatmap(samples.integrated.filt, features = rownames(samples.integrated.filt)[moduleColors==m], slot="counts",
          group.by = "Genotype_Treatment")+scale_fill_viridis()

ggsave(paste0("/files/scSeq_Hefendehl/output/WGCNAGeneWise_ExpressionModule", m, ".svg"), p)
}

Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
Saving 7 x 5 in image
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
Saving 7 x 5 in image
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
Saving 7 x 5 in image
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
Saving 7 x 5 in image
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
Saving 7 x 5 in image

WGCNA GO terms

genelist = split(names(net$colors), net$colors)
gene_univers = names(net$colors)

pvallist = unlist(reslist)
MOI = gsub("ME", "", names(pvallist)) %>% gsub(".Genotype_Treatment", "", .)
# MOI = MOI[pvallist<0.05]

# if you want to have the affected genes set return_genelist = T; this takes roughly 4-8 hours 

Gores = getGOresults(geneset = genelist[MOI], domain_scope = "known",
                     genereference = gene_univers, 
                     return_genelist = T,
                     organism = "mmusculus")

Detected custom background input, domain scope is set to 'custom'

display_tab(Gores$result)

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

gplot = gprofiler2::gostplot(Gores)
gplot$width = 1000
gplot$height = 1200

gplot %>% layout(width = 500, height = 2500)

Warning: Specifying width/height in layout() is now deprecated.
Please specify in ggplotly() or plot_ly()

save.image("/files/scSeq_Hefendehl/output/tmp.RData")

not done

4. The Svg file works, we can edit in Illustrator, thanks. Could you please add the “Celltype” and “Genotype_Treatment” clusters just for the microglia clusters without mac/mono and granulocytes?
5. Can you try to extract just the clustering tree of the hierarchical clustering and label it accordingly to make it potentially less “busy”? (of “Stroke_spatial_binary” and “distal_spatial_binary”)

Session information

sessionInfo()

R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8          LC_NUMERIC=C                 
 [3] LC_TIME=en_US.UTF-8           LC_COLLATE=en_US.UTF-8       
 [5] LC_MONETARY=en_US.UTF-8       LC_MESSAGES=en_US.UTF-8      
 [7] LC_PAPER=en_US.UTF-8          LC_NAME=en_US.UTF-8          
 [9] LC_ADDRESS=en_US.UTF-8        LC_TELEPHONE=en_US.UTF-8     
[11] LC_MEASUREMENT=en_US.UTF-8    LC_IDENTIFICATION=en_US.UTF-8

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] TSCAN_1.36.0                ggbeeswarm_0.7.2           
 [3] scSorter_0.0.2              chisq.posthoc.test_0.1.2   
 [5] gprofiler2_0.2.2            celldex_1.8.0              
 [7] ensembldb_2.22.0            AnnotationFilter_1.22.0    
 [9] GenomicFeatures_1.50.4      lm.beta_1.7-2              
[11] pheatmap_1.0.12             RColorBrewer_1.1-3         
[13] kableExtra_1.3.4            org.Mm.eg.db_3.16.0        
[15] AnnotationDbi_1.62.2        plotly_4.10.3              
[17] WGCNA_1.72-1                fastcluster_1.2.3          
[19] dynamicTreeCut_1.63-1       AnnotationHub_3.6.0        
[21] BiocFileCache_2.11.1        dbplyr_2.4.0               
[23] RCurl_1.98-1.13             clustree_0.5.1             
[25] ggraph_2.1.0                ggpubr_0.6.0               
[27] slingshot_2.6.0             TrajectoryUtils_1.6.0      
[29] SingleCellExperiment_1.20.1 princurve_2.1.6            
[31] DT_0.30                     viridis_0.6.4              
[33] viridisLite_0.4.2           xlsx_0.6.5                 
[35] brms_2.20.4                 compareGroups_4.7.2        
[37] data.table_1.14.8           SingleR_2.0.0              
[39] harmony_1.1.0               Rcpp_1.0.11                
[41] Seurat_5.0.1                SeuratObject_5.0.1         
[43] sp_2.1-1                    lubridate_1.9.3            
[45] forcats_1.0.0               stringr_1.5.1              
[47] dplyr_1.1.4                 purrr_1.0.2                
[49] readr_2.1.4                 tidyr_1.3.0                
[51] tibble_3.2.1                ggplot2_3.4.4              
[53] tidyverse_2.0.0             DESeq2_1.38.3              
[55] SummarizedExperiment_1.28.0 Biobase_2.58.0             
[57] MatrixGenerics_1.10.0       matrixStats_1.1.0          
[59] GenomicRanges_1.50.2        GenomeInfoDb_1.34.9        
[61] IRanges_2.32.0              S4Vectors_0.36.2           
[63] BiocGenerics_0.44.0         limma_3.54.2               
[65] workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] QuickJSR_1.0.7                graphlayouts_1.0.2           
  [3] pbapply_1.7-2                 lattice_0.22-5               
  [5] rJava_1.0-6                   fontquiver_0.2.1             
  [7] vctrs_0.6.4                   V8_4.4.0                     
  [9] fastICA_1.2-3                 mgcv_1.9-0                   
 [11] blob_1.2.4                    survival_3.5-7               
 [13] spatstat.data_3.0-3           later_1.3.1                  
 [15] nloptr_2.0.3                  DBI_1.1.3                    
 [17] gfonts_0.2.0                  rappdirs_0.3.3               
 [19] uwot_0.1.16                   dqrng_0.3.1                  
 [21] distributional_0.3.2          zlibbioc_1.44.0              
 [23] htmlwidgets_1.6.3             mvtnorm_1.2-3                
 [25] future_1.33.0                 inline_0.3.19                
 [27] leiden_0.4.3.1                scater_1.26.1                
 [29] parallel_4.2.2                irlba_2.3.5.1                
 [31] markdown_1.11                 tidygraph_1.2.3              
 [33] KernSmooth_2.23-22            promises_1.2.1               
 [35] DelayedArray_0.24.0           RcppParallel_5.1.7           
 [37] Hmisc_5.1-1                   RSpectra_0.16-1              
 [39] fs_1.6.3                      textshaping_0.3.7            
 [41] presto_1.0.0                  RhpcBLASctl_0.23-42          
 [43] digest_0.6.33                 png_0.1-8                    
 [45] bluster_1.8.0                 sctransform_0.4.1            
 [47] cowplot_1.1.1                 glmnet_4.1-8                 
 [49] crul_1.4.0                    pkgconfig_2.0.3              
 [51] GO.db_3.17.0                  spatstat.random_3.2-1        
 [53] DelayedMatrixStats_1.20.0     dygraphs_1.1.1.6             
 [55] iterators_1.0.14              minqa_1.2.6                  
 [57] reticulate_1.34.0             rstan_2.32.3                 
 [59] beeswarm_0.4.0                spam_2.10-0                  
 [61] xfun_0.41                     bslib_0.6.0                  
 [63] zoo_1.8-12                    tidyselect_1.2.0             
 [65] reshape2_1.4.4                ica_1.0-3                    
 [67] rtracklayer_1.58.0            pkgbuild_1.4.2               
 [69] rlang_1.1.2                   isoband_0.2.7                
 [71] HardyWeinberg_1.7.5           jquerylib_0.1.4              
 [73] glue_1.6.2                    gdtools_0.3.4                
 [75] ggsignif_0.6.4                threejs_0.3.3                
 [77] labeling_0.4.3                fontLiberation_0.1.0         
 [79] httpuv_1.6.12                 preprocessCore_1.62.1        
 [81] BiocNeighbors_1.16.0          shinystan_2.6.0              
 [83] annotate_1.76.0               webshot_0.5.5                
 [85] jsonlite_1.8.7                XVector_0.38.0               
 [87] bit_4.0.5                     fontBitstreamVera_0.1.1      
 [89] mime_0.12                     systemfonts_1.0.5            
 [91] gplots_3.1.3                  Rsamtools_2.14.0             
 [93] gridExtra_2.3                 stringi_1.8.1                
 [95] processx_3.8.2                spatstat.sparse_3.0-3        
 [97] scattermore_1.2               spatstat.explore_3.2-5       
 [99] bitops_1.0-7                  cli_3.6.1                    
[101] RSQLite_2.3.3                 timechange_0.2.0             
[103] officer_0.6.3                 rstudioapi_0.15.0            
[105] mitml_0.4-5                   GenomicAlignments_1.34.1     
[107] git2r_0.32.0                  nlme_3.1-163                 
[109] scran_1.26.2                  locfit_1.5-9.8               
[111] listenv_0.9.0                 miniUI_0.1.1.1               
[113] pan_1.9                       httpcode_0.3.0               
[115] lifecycle_1.0.4               ExperimentHub_2.6.0          
[117] posterior_1.5.0               munsell_0.5.0                
[119] caTools_1.18.2                codetools_0.2-19             
[121] coda_0.19-4                   vipor_0.4.5                  
[123] lmtest_0.9-40                 bayesplot_1.10.0             
[125] xlsxjars_0.6.1                htmlTable_2.4.2              
[127] rstantools_2.3.1.1            xtable_1.8-4                 
[129] ROCR_1.0-11                   BiocManager_1.30.22          
[131] StanHeaders_2.26.28           abind_1.4-5                  
[133] farver_2.1.1                  parallelly_1.36.0            
[135] RANN_2.6.1                    askpass_1.2.0                
[137] BiocIO_1.8.0                  RcppAnnoy_0.0.21             
[139] goftest_1.2-3                 patchwork_1.1.3              
[141] shinythemes_1.2.0             cluster_2.1.4                
[143] future.apply_1.11.0           Matrix_1.6-3                 
[145] ellipsis_0.3.2                prettyunits_1.2.0            
[147] ggridges_0.5.4                mclust_6.0.1                 
[149] flextable_0.9.4               igraph_1.5.1                 
[151] shinyjs_2.1.0                 spatstat.utils_3.0-4         
[153] htmltools_0.5.7               yaml_2.3.7                   
[155] loo_2.6.0                     utf8_1.2.4                   
[157] interactiveDisplayBase_1.36.0 XML_3.99-0.15                
[159] jomo_2.7-6                    scuttle_1.8.4                
[161] foreign_0.8-85                withr_2.5.2                  
[163] fitdistrplus_1.1-11           BiocParallel_1.32.6          
[165] bit64_4.0.5                   foreach_1.5.2                
[167] ProtGenerics_1.30.0           Biostrings_2.66.0            
[169] combinat_0.0-8                progressr_0.14.0             
[171] ragg_1.2.6                    rsvd_1.0.5                   
[173] ScaledMatrix_1.6.0            memoise_2.0.1                
[175] evaluate_0.23                 geneplotter_1.76.0           
[177] tzdb_0.4.0                    callr_3.7.3                  
[179] ps_1.7.5                      curl_5.1.0                   
[181] fansi_1.0.5                   fastDummies_1.7.3            
[183] highr_0.10                    xts_0.13.1                   
[185] edgeR_3.40.2                  tensor_1.5                   
[187] checkmate_2.3.0               cachem_1.0.8                 
[189] truncnorm_1.0-9               deldir_1.0-9                 
[191] tensorA_0.36.2                impute_1.74.1                
[193] metapod_1.6.0                 rjson_0.2.21                 
[195] openxlsx_4.2.5.2              rstatix_0.7.2                
[197] getPass_0.2-2                 ggrepel_0.9.4                
[199] glmGamPoi_1.10.2              rprojroot_2.0.4              
[201] tools_4.2.2                   sass_0.4.7                   
[203] magrittr_2.0.3                Rsolnp_1.16                  
[205] mice_3.16.0                   car_3.1-2                    
[207] xml2_1.3.5                    httr_1.4.7                   
[209] rmarkdown_2.25                boot_1.3-28.1                
[211] globals_0.16.2                R6_2.5.1                     
[213] nnet_7.3-19                   RcppHNSW_0.5.0               
[215] progress_1.2.2                Brobdingnag_1.2-9            
[217] KEGGREST_1.38.0               statmod_1.5.0                
[219] gtools_3.9.5                  shape_1.4.6                  
[221] beachmat_2.14.2               BiocVersion_3.16.0           
[223] BiocSingular_1.14.0           ggrastr_1.0.2                
[225] splines_4.2.2                 carData_3.0-5                
[227] colorspace_2.1-0              generics_0.1.3               
[229] base64enc_0.1-3               chron_2.3-61                 
[231] pillar_1.9.0                  tweenr_2.0.2                 
[233] uuid_1.1-1                    GenomeInfoDbData_1.2.9       
[235] plyr_1.8.9                    dotCall64_1.1-0              
[237] gtable_0.3.4                  rvest_1.0.3                  
[239] zip_2.3.0                     restfulr_0.0.15              
[241] colourpicker_1.3.0            knitr_1.45                   
[243] biomaRt_2.54.1                fastmap_1.1.1                
[245] crosstalk_1.2.0               doParallel_1.0.17            
[247] broom_1.0.5                   openssl_2.1.1                
[249] scales_1.2.1                  filelock_1.0.2               
[251] backports_1.4.1               writexl_1.4.2                
[253] EnhancedVolcano_1.16.0        lme4_1.1-35.1                
[255] hms_1.1.3                     ggforce_0.4.1                
[257] Rtsne_0.16                    shiny_1.8.0                  
[259] polyclip_1.10-6               grid_4.2.2                   
[261] lazyeval_0.2.2                Formula_1.2-5                
[263] whisker_0.4.1                 crayon_1.5.2                 
[265] MASS_7.3-60                   sparseMatrixStats_1.10.0     
[267] bridgesampling_1.1-2          svglite_2.1.2                
[269] rpart_4.1.21                  compiler_4.2.2               
[271] spatstat.geom_3.2-7